From daaf7c5c0160ea1932fd30a875e4395451532c6b Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Fri, 5 Jul 2024 09:54:07 +0000 Subject: [PATCH 01/22] Test from wing website --- content/authors/alumnus/_index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/authors/alumnus/_index.md b/content/authors/alumnus/_index.md index 7fa329a..6d11b00 100644 --- a/content/authors/alumnus/_index.md +++ b/content/authors/alumnus/_index.md @@ -3,8 +3,8 @@ title: Somebody # Full Name (for SEO) -first_name: Min-Yen -last_name: Kan +first_name: Somebody +last_name: Somewhere # Is this the primary user of the site? superuser: false @@ -14,9 +14,9 @@ superuser: false # For an email link, use "fas" icon pack, "envelope" icon, and a link in the # form "mailto:your-email@example.com" or "#contact" for contact widget. social: - - icon: house - icon_pack: fas - link: https://www.comp.nus.edu.sg/~kanmy/ + # - icon: house + # icon_pack: fas + # link: https://www.comp.nus.edu.sg/~kanmy/ # Highlight the author in author lists? (true/false) highlight_name: false From a8b372dc5819d5e8e171b606fa6ea34b0a16e77c Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Fri, 5 Jul 2024 10:14:53 +0000 Subject: [PATCH 02/22] test ingestion pipeline --- content/authors/alumnus/_index.md | 3 +-- publications.bib | 27 +++++++++++++++++++++++++++ 2 files changed, 28 insertions(+), 2 deletions(-) create mode 100644 publications.bib diff --git a/content/authors/alumnus/_index.md b/content/authors/alumnus/_index.md index 6d11b00..20642e7 100644 --- a/content/authors/alumnus/_index.md +++ b/content/authors/alumnus/_index.md @@ -26,6 +26,5 @@ highlight_name: false user_groups: - Alumni ---- - +---xw An alumnus \ No newline at end of file diff --git a/publications.bib b/publications.bib new file mode 100644 index 0000000..3a8ab8a --- /dev/null +++ b/publications.bib @@ -0,0 +1,27 @@ +@inproceedings{huang-etal-2022-lightweight, + title = "Lightweight Contextual Logical Structure Recovery", + author = "Huang, Po-Wei and + Ramesh Kashyap, Abhinav and + Qin, Yanxia and + Yang, Yajing and + Kan, Min-Yen", + editor = "Cohan, Arman and + Feigenblat, Guy and + Freitag, Dayne and + Ghosal, Tirthankar and + Herrmannova, Drahomira and + Knoth, Petr and + Lo, Kyle and + Mayr, Philipp and + Shmueli-Scheuer, Michal and + de Waard, Anita and + Wang, Lucy Lu", + booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing", + month = oct, + year = "2022", + address = "Gyeongju, Republic of Korea", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.sdp-1.5", + pages = "37--48", + abstract = "Logical structure recovery in scientific articles associates text with a semantic section of the article. Although previous work has disregarded the surrounding context of a line, we model this important information by employing line-level attention on top of a transformer-based scientific document processing pipeline. With the addition of loss function engineering and data augmentation techniques with semi-supervised learning, our method improves classification performance by 10{\%} compared to a recent state-of-the-art model. Our parsimonious, text-only method achieves a performance comparable to that of other works that use rich document features such as font and spatial position, using less data without sacrificing performance, resulting in a lightweight training pipeline.", +} From 6001ceb385ff0066d2b20d9a65268002621ac7ed Mon Sep 17 00:00:00 2001 From: knmnyn <217067+knmnyn@users.noreply.github.com> Date: Fri, 5 Jul 2024 10:15:27 +0000 Subject: [PATCH 03/22] content: import publications from Bibtex --- .../huang-etal-2022-lightweight/cite.bib | 27 +++++++++++++++++++ .../huang-etal-2022-lightweight/index.md | 27 +++++++++++++++++++ 2 files changed, 54 insertions(+) create mode 100644 content/publication/huang-etal-2022-lightweight/cite.bib create mode 100644 content/publication/huang-etal-2022-lightweight/index.md diff --git a/content/publication/huang-etal-2022-lightweight/cite.bib b/content/publication/huang-etal-2022-lightweight/cite.bib new file mode 100644 index 0000000..d803db8 --- /dev/null +++ b/content/publication/huang-etal-2022-lightweight/cite.bib @@ -0,0 +1,27 @@ +@inproceedings{huang-etal-2022-lightweight, + abstract = {Logical structure recovery in scientific articles associates text with a semantic section of the article. Although previous work has disregarded the surrounding context of a line, we model this important information by employing line-level attention on top of a transformer-based scientific document processing pipeline. With the addition of loss function engineering and data augmentation techniques with semi-supervised learning, our method improves classification performance by 10% compared to a recent state-of-the-art model. Our parsimonious, text-only method achieves a performance comparable to that of other works that use rich document features such as font and spatial position, using less data without sacrificing performance, resulting in a lightweight training pipeline.}, + address = {Gyeongju, Republic of Korea}, + author = {Huang, Po-Wei and +Ramesh Kashyap, Abhinav and +Qin, Yanxia and +Yang, Yajing and +Kan, Min-Yen}, + booktitle = {Proceedings of the Third Workshop on Scholarly Document Processing}, + editor = {Cohan, Arman and +Feigenblat, Guy and +Freitag, Dayne and +Ghosal, Tirthankar and +Herrmannova, Drahomira and +Knoth, Petr and +Lo, Kyle and +Mayr, Philipp and +Shmueli-Scheuer, Michal and +de Waard, Anita and +Wang, Lucy Lu}, + month = {October}, + pages = {37--48}, + publisher = {Association for Computational Linguistics}, + title = {Lightweight Contextual Logical Structure Recovery}, + url = {https://aclanthology.org/2022.sdp-1.5}, + year = {2022} +} diff --git a/content/publication/huang-etal-2022-lightweight/index.md b/content/publication/huang-etal-2022-lightweight/index.md new file mode 100644 index 0000000..d0efc15 --- /dev/null +++ b/content/publication/huang-etal-2022-lightweight/index.md @@ -0,0 +1,27 @@ +--- +title: Lightweight Contextual Logical Structure Recovery +authors: +- Po-Wei Huang +- Abhinav Ramesh Kashyap +- Yanxia Qin +- Yajing Yang +- Min-Yen Kan +date: '2022-10-01' +publishDate: '2024-07-05T10:15:26.841390Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Third Workshop on Scholarly Document Processing*' +abstract: Logical structure recovery in scientific articles associates text with a + semantic section of the article. Although previous work has disregarded the surrounding + context of a line, we model this important information by employing line-level attention + on top of a transformer-based scientific document processing pipeline. With the + addition of loss function engineering and data augmentation techniques with semi-supervised + learning, our method improves classification performance by 10% compared to a recent + state-of-the-art model. Our parsimonious, text-only method achieves a performance + comparable to that of other works that use rich document features such as font and + spatial position, using less data without sacrificing performance, resulting in + a lightweight training pipeline. +links: +- name: URL + url: https://aclanthology.org/2022.sdp-1.5 +--- From 10dc7d2ed72953c436b9d0c39cf1c042e6c61433 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Fri, 5 Jul 2024 10:20:48 +0000 Subject: [PATCH 04/22] Remove old publication ingestion --- publications.bib | 27 --------------------------- 1 file changed, 27 deletions(-) delete mode 100644 publications.bib diff --git a/publications.bib b/publications.bib deleted file mode 100644 index 3a8ab8a..0000000 --- a/publications.bib +++ /dev/null @@ -1,27 +0,0 @@ -@inproceedings{huang-etal-2022-lightweight, - title = "Lightweight Contextual Logical Structure Recovery", - author = "Huang, Po-Wei and - Ramesh Kashyap, Abhinav and - Qin, Yanxia and - Yang, Yajing and - Kan, Min-Yen", - editor = "Cohan, Arman and - Feigenblat, Guy and - Freitag, Dayne and - Ghosal, Tirthankar and - Herrmannova, Drahomira and - Knoth, Petr and - Lo, Kyle and - Mayr, Philipp and - Shmueli-Scheuer, Michal and - de Waard, Anita and - Wang, Lucy Lu", - booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing", - month = oct, - year = "2022", - address = "Gyeongju, Republic of Korea", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.sdp-1.5", - pages = "37--48", - abstract = "Logical structure recovery in scientific articles associates text with a semantic section of the article. Although previous work has disregarded the surrounding context of a line, we model this important information by employing line-level attention on top of a transformer-based scientific document processing pipeline. With the addition of loss function engineering and data augmentation techniques with semi-supervised learning, our method improves classification performance by 10{\%} compared to a recent state-of-the-art model. Our parsimonious, text-only method achieves a performance comparable to that of other works that use rich document features such as font and spatial position, using less data without sacrificing performance, resulting in a lightweight training pipeline.", -} From 35dad044699a3840b2e95ad2bbd482dd3d3e9ce5 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Fri, 5 Jul 2024 17:09:21 +0000 Subject: [PATCH 05/22] Try to ingest multiple entries. --- publications.bib | 234 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 234 insertions(+) create mode 100644 publications.bib diff --git a/publications.bib b/publications.bib new file mode 100644 index 0000000..f1de23a --- /dev/null +++ b/publications.bib @@ -0,0 +1,234 @@ +@inproceedings{han-etal-2024-self, + title = "Self-Adaptive Sampling for Accurate Video Question Answering on Image Text Models", + author = "Han, Wei and + Chen, Hui and + Kan, Min-Yen and + Poria, Soujanya", + editor = "Duh, Kevin and + Gomez, Helena and + Bethard, Steven", + booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024", + month = jun, + year = "2024", + address = "Mexico City, Mexico", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2024.findings-naacl.162", + pages = "2522--2534", + abstract = "Image{--}text models (ITMs) is the prevalent architecture to solve video question{--}answering tasks, which requires only a few input frames to save huge computational cost compared to video{--}language models.However, we find existent ITM video question{--}answering solutions either 1) adopt simplistic and unintentional sampling strategies, which may miss key frames to offer the answer clues; or 2) sample a large number of frames into divided groups, which the computational sources can not accommodate. In this work, we aim at an efficient sampling method towards the few-frame situations.We first summarize a family of prior sampling methods based on question{--}frame correlation into a unified one, dubbed *Most Implied Frames* (MIF). Through some primary results and analysis, Through analysis, we form a hypothesis that question-aware sampling is not necessary, from which we further propose the other method *Most Dominant Frames* (MDF).Experimental results on four public datasets and three advanced ITMs demonstrate that our proposed strategies can boost the performance for image{--}text pretrained models, and have a wide application scenario in terms of model architectures and dataset types. Our code is available at https://github.com/declare-lab/Sealing\url{https://github.com/declare-lab/Sealing}.", +} + +@inproceedings{aksu-etal-2022-n, + title = "N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking", + author = "Aksu, Ibrahim and + Liu, Zhengyuan and + Kan, Min-Yen and + Chen, Nancy", + editor = "Muresan, Smaranda and + Nakov, Preslav and + Villavicencio, Aline", + booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", + month = may, + year = "2022", + address = "Dublin, Ireland", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.findings-acl.131", + doi = "10.18653/v1/2022.findings-acl.131", + pages = "1659--1671", + abstract = "Augmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure. In this work, we introduce an augmentation framework that utilizes belief state annotations to match turns from various dialogues and form new synthetic dialogues in a bottom-up manner. Unlike other augmentation strategies, it operates with as few as five examples. Our augmentation strategy yields significant improvements when both adapting a DST model to a new domain, and when adapting a language model to the DST task, on evaluations with TRADE and TOD-BERT models. Further analysis shows that our model performs better on seen values during training, and it is also more robust to unseen values. We conclude that exploiting belief state annotations enhances dialogue augmentation and results in improved models in n-shot training scenarios.", +} + +@inproceedings{zhang-etal-2022-interpreting, + title = "Interpreting the Robustness of Neural {NLP} Models to Textual Perturbations", + author = "Zhang, Yunxiang and + Pan, Liangming and + Tan, Samson and + Kan, Min-Yen", + editor = "Muresan, Smaranda and + Nakov, Preslav and + Villavicencio, Aline", + booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", + month = may, + year = "2022", + address = "Dublin, Ireland", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.findings-acl.315", + doi = "10.18653/v1/2022.findings-acl.315", + pages = "3993--4007", + abstract = "Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear why models are less robust to some perturbations than others. In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence). We further give a causal justification for the learnability metric. We conduct extensive experiments with four prominent NLP models {---} TextRNN, BERT, RoBERTa and XLNet {---} over eight types of textual perturbations on three datasets. We show that a model which is better at identifying a perturbation (higher learnability) becomes worse at ignoring such a perturbation at test time (lower robustness), providing empirical support for our hypothesis.", +} + +@inproceedings{dou-etal-2022-towards, + title = "Towards Knowledge-Intensive Text-to-{SQL} Semantic Parsing with Formulaic Knowledge", + author = "Dou, Longxu and + Gao, Yan and + Liu, Xuqi and + Pan, Mingyang and + Wang, Dingzirui and + Che, Wanxiang and + Zhan, Dechen and + Kan, Min-Yen and + Lou, Jian-Guang", + editor = "Goldberg, Yoav and + Kozareva, Zornitsa and + Zhang, Yue", + booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", + month = dec, + year = "2022", + address = "Abu Dhabi, United Arab Emirates", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.emnlp-main.350", + doi = "10.18653/v1/2022.emnlp-main.350", + pages = "5240--5253", + abstract = "In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by representing formulaic knowledge rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2{\%} improvement overall on KnowSQL.", +} + +@inproceedings{han-etal-2022-mm, + title = "{MM}-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences", + author = "Han, Wei and + Chen, Hui and + Kan, Min-Yen and + Poria, Soujanya", + editor = "Goldberg, Yoav and + Kozareva, Zornitsa and + Zhang, Yue", + booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", + month = dec, + year = "2022", + address = "Abu Dhabi, United Arab Emirates", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.emnlp-main.717", + doi = "10.18653/v1/2022.emnlp-main.717", + pages = "10498--10511", + abstract = "Existing multimodal tasks mostly target at the complete input modality setting, i.e., each modality is either complete or completely missing in both training and test sets. However, the randomly missing situations have still been underexplored. In this paper, we present a novel approach named MM-Align to address the missing-modality inference problem. Concretely, we propose 1) an alignment dynamics learning module based on the theory of optimal transport (OT) for missing data imputation; 2) a denoising training algorithm to enhance the quality of imputation as well as the accuracy of model predictions. Compared with previous generative methods which devote to restoring the missing inputs, MM-Align learns to capture and imitate the alignment dynamics between modality sequences. Results of comprehensive experiments on two multimodal tasks empirically demonstrate that our method can perform more accurate and faster inference and alleviate the overfitting issue under different missing conditions.", +} + +@inproceedings{jain-etal-2022-comparative, + title = "Comparative Snippet Generation", + author = "Jain, Saurabh and + Miao, Yisong and + Kan, Min-Yen", + editor = "Malmasi, Shervin and + Rokhlenko, Oleg and + Ueffing, Nicola and + Guy, Ido and + Agichtein, Eugene and + Kallumadi, Surya", + booktitle = "Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)", + month = may, + year = "2022", + address = "Dublin, Ireland", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.ecnlp-1.7", + doi = "10.18653/v1/2022.ecnlp-1.7", + pages = "49--57", + abstract = "We model products{'} reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generation from contrasting opinions regarding a product, and an analysis of performance of a pre-trained BERT model to generate such snippets.", +} + +@inproceedings{xu-etal-2022-corefdiffs, + title = "{C}oref{D}iffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations", + author = "Xu, Lin and + Zhou, Qixian and + Fu, Jinlan and + Kan, Min-Yen and + Ng, See-Kiong", + editor = "Calzolari, Nicoletta and + Huang, Chu-Ren and + Kim, Hansaem and + Pustejovsky, James and + Wanner, Leo and + Choi, Key-Sun and + Ryu, Pum-Mo and + Chen, Hsin-Hsi and + Donatelli, Lucia and + Ji, Heng and + Kurohashi, Sadao and + Paggio, Patrizia and + Xue, Nianwen and + Kim, Seokhwan and + Hahm, Younggyun and + He, Zhong and + Lee, Tony Kyungil and + Santus, Enrico and + Bond, Francis and + Na, Seung-Hoon", + booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", + month = oct, + year = "2022", + address = "Gyeongju, Republic of Korea", + publisher = "International Committee on Computational Linguistics", + url = "https://aclanthology.org/2022.coling-1.38", + pages = "471--484", + abstract = "Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5{\%}, 7.4{\%} and 8.2{\%} on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation.", +} + +@inproceedings{ramesh-kashyap-etal-2022-different, + title = "So Different Yet So Alike! Constrained Unsupervised Text Style Transfer", + author = "Ramesh Kashyap, Abhinav and + Hazarika, Devamanyu and + Kan, Min-Yen and + Zimmermann, Roger and + Poria, Soujanya", + editor = "Muresan, Smaranda and + Nakov, Preslav and + Villavicencio, Aline", + booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = may, + year = "2022", + address = "Dublin, Ireland", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.acl-long.32", + doi = "10.18653/v1/2022.acl-long.32", + pages = "416--431", + abstract = "Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content while adapting to the target domain. However, it does not explicitly maintain other attributes between the source and translated text: e.g., text length and descriptiveness. Maintaining constraints in transfer has several downstream applications, including data augmentation and debiasing. We introduce a method for such constrained unsupervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooperate and reduce the same loss. The first is a contrastive loss and the second is a classification loss {---} aiming to regularize the latent space further and bring similar sentences closer together. We demonstrate that such training retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures.", +} + +@inproceedings{qin-etal-2022-gl, + title = "{GL}-{CL}e{F}: A Global{--}Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding", + author = "Qin, Libo and + Chen, Qiguang and + Xie, Tianbao and + Li, Qixin and + Lou, Jian-Guang and + Che, Wanxiang and + Kan, Min-Yen", + editor = "Muresan, Smaranda and + Nakov, Preslav and + Villavicencio, Aline", + booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = may, + year = "2022", + address = "Dublin, Ireland", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.acl-long.191", + doi = "10.18653/v1/2022.acl-long.191", + pages = "2677--2686", + abstract = "Due to high data demands of current methods, attention to zero-shot cross-lingual spoken language understanding (SLU) has grown, as such approaches greatly reduce human annotation effort. However, existing models solely rely on shared parameters, which can only perform implicit alignment across languages. We present Global-Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming. Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages. In addition, a key step in GL-CLeF is a proposed Local and Global component, which achieves a fine-grained cross-lingual transfer (i.e., sentence-level Local intent transfer, token-level Local slot transfer, and semantic-level Global transfer across intent and slot). Experiments on MultiATIS++ show that GL-CLeF achieves the best performance and successfully pulls representations of similar sentences across languages closer.", +} + +@inproceedings{aksu-etal-2021-velocidapter, + title = "Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation", + author = "Aksu, Ibrahim Taha and + Liu, Zhengyuan and + Kan, Min-Yen and + Chen, Nancy", + editor = "Li, Haizhou and + Levow, Gina-Anne and + Yu, Zhou and + Gupta, Chitralekha and + Sisman, Berrak and + Cai, Siqi and + Vandyke, David and + Dethlefs, Nina and + Wu, Yan and + Li, Junyi Jessy", + booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue", + month = jul, + year = "2021", + address = "Singapore and Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.sigdial-1.14", + doi = "10.18653/v1/2021.sigdial-1.14", + pages = "133--143", + abstract = "We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension. Velocidapter augments datasets by simulating synthetic conversations for a task-oriented dialogue domain, requiring a small amount of bootstrapping work for each new domain. We evaluate the efficacy of our framework on a task-oriented dialogue comprehension dataset, MRCWOZ, which we curate by annotating questions for slots in the restaurant, taxi, and hotel domains of the MultiWOZ 2.2 dataset (Zang et al., 2020). We run experiments within a low-resource setting, where we pretrain a model on SQuAD, fine-tuning it on either a small original data or on the synthetic data generated by our framework. Velocidapter shows significant improvements using both the transformer-based BERTBase and BiDAF as base models. We further show that the framework is easy to use by novice users and conclude that Velocidapter can greatly help training over task-oriented dialogues, especially for low-resourced emerging domains.", +} + From b5a87db4c450f36090462f295e4ebc04a2cab11d Mon Sep 17 00:00:00 2001 From: knmnyn <217067+knmnyn@users.noreply.github.com> Date: Fri, 5 Jul 2024 17:09:43 +0000 Subject: [PATCH 06/22] content: import publications from Bibtex --- .../aksu-etal-2021-velocidapter/cite.bib | 26 ++++++++++++++ .../aksu-etal-2021-velocidapter/index.md | 32 +++++++++++++++++ content/publication/aksu-etal-2022-n/cite.bib | 19 ++++++++++ content/publication/aksu-etal-2022-n/index.md | 29 +++++++++++++++ .../dou-etal-2022-towards/cite.bib | 24 +++++++++++++ .../dou-etal-2022-towards/index.md | 32 +++++++++++++++++ content/publication/han-etal-2022-mm/cite.bib | 19 ++++++++++ content/publication/han-etal-2022-mm/index.md | 32 +++++++++++++++++ .../publication/han-etal-2024-self/cite.bib | 18 ++++++++++ .../publication/han-etal-2024-self/index.md | 32 +++++++++++++++++ .../jain-etal-2022-comparative/cite.bib | 21 +++++++++++ .../jain-etal-2022-comparative/index.md | 22 ++++++++++++ content/publication/qin-etal-2022-gl/cite.bib | 22 ++++++++++++ content/publication/qin-etal-2022-gl/index.md | 36 +++++++++++++++++++ .../cite.bib | 20 +++++++++++ .../index.md | 34 ++++++++++++++++++ .../xu-etal-2022-corefdiffs/cite.bib | 36 +++++++++++++++++++ .../xu-etal-2022-corefdiffs/index.md | 31 ++++++++++++++++ .../zhang-etal-2022-interpreting/cite.bib | 19 ++++++++++ .../zhang-etal-2022-interpreting/index.md | 29 +++++++++++++++ 20 files changed, 533 insertions(+) create mode 100644 content/publication/aksu-etal-2021-velocidapter/cite.bib create mode 100644 content/publication/aksu-etal-2021-velocidapter/index.md create mode 100644 content/publication/aksu-etal-2022-n/cite.bib create mode 100644 content/publication/aksu-etal-2022-n/index.md create mode 100644 content/publication/dou-etal-2022-towards/cite.bib create mode 100644 content/publication/dou-etal-2022-towards/index.md create mode 100644 content/publication/han-etal-2022-mm/cite.bib create mode 100644 content/publication/han-etal-2022-mm/index.md create mode 100644 content/publication/han-etal-2024-self/cite.bib create mode 100644 content/publication/han-etal-2024-self/index.md create mode 100644 content/publication/jain-etal-2022-comparative/cite.bib create mode 100644 content/publication/jain-etal-2022-comparative/index.md create mode 100644 content/publication/qin-etal-2022-gl/cite.bib create mode 100644 content/publication/qin-etal-2022-gl/index.md create mode 100644 content/publication/ramesh-kashyap-etal-2022-different/cite.bib create mode 100644 content/publication/ramesh-kashyap-etal-2022-different/index.md create mode 100644 content/publication/xu-etal-2022-corefdiffs/cite.bib create mode 100644 content/publication/xu-etal-2022-corefdiffs/index.md create mode 100644 content/publication/zhang-etal-2022-interpreting/cite.bib create mode 100644 content/publication/zhang-etal-2022-interpreting/index.md diff --git a/content/publication/aksu-etal-2021-velocidapter/cite.bib b/content/publication/aksu-etal-2021-velocidapter/cite.bib new file mode 100644 index 0000000..0603cb7 --- /dev/null +++ b/content/publication/aksu-etal-2021-velocidapter/cite.bib @@ -0,0 +1,26 @@ +@inproceedings{aksu-etal-2021-velocidapter, + abstract = {We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension. Velocidapter augments datasets by simulating synthetic conversations for a task-oriented dialogue domain, requiring a small amount of bootstrapping work for each new domain. We evaluate the efficacy of our framework on a task-oriented dialogue comprehension dataset, MRCWOZ, which we curate by annotating questions for slots in the restaurant, taxi, and hotel domains of the MultiWOZ 2.2 dataset (Zang et al., 2020). We run experiments within a low-resource setting, where we pretrain a model on SQuAD, fine-tuning it on either a small original data or on the synthetic data generated by our framework. Velocidapter shows significant improvements using both the transformer-based BERTBase and BiDAF as base models. We further show that the framework is easy to use by novice users and conclude that Velocidapter can greatly help training over task-oriented dialogues, especially for low-resourced emerging domains.}, + address = {Singapore and Online}, + author = {Aksu, Ibrahim Taha and +Liu, Zhengyuan and +Kan, Min-Yen and +Chen, Nancy}, + booktitle = {Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue}, + doi = {10.18653/v1/2021.sigdial-1.14}, + editor = {Li, Haizhou and +Levow, Gina-Anne and +Yu, Zhou and +Gupta, Chitralekha and +Sisman, Berrak and +Cai, Siqi and +Vandyke, David and +Dethlefs, Nina and +Wu, Yan and +Li, Junyi Jessy}, + month = {July}, + pages = {133--143}, + publisher = {Association for Computational Linguistics}, + title = {Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation}, + url = {https://aclanthology.org/2021.sigdial-1.14}, + year = {2021} +} diff --git a/content/publication/aksu-etal-2021-velocidapter/index.md b/content/publication/aksu-etal-2021-velocidapter/index.md new file mode 100644 index 0000000..82f7444 --- /dev/null +++ b/content/publication/aksu-etal-2021-velocidapter/index.md @@ -0,0 +1,32 @@ +--- +title: 'Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic + Text Generation with Domain Adaptation' +authors: +- Ibrahim Taha Aksu +- Zhengyuan Liu +- Min-Yen Kan +- Nancy Chen +date: '2021-07-01' +publishDate: '2024-07-05T17:09:42.645613Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 22nd Annual Meeting of the Special Interest Group + on Discourse and Dialogue*' +doi: 10.18653/v1/2021.sigdial-1.14 +abstract: We introduce a synthetic dialogue generation framework, Velocidapter, which + addresses the corpus availability problem for dialogue comprehension. Velocidapter + augments datasets by simulating synthetic conversations for a task-oriented dialogue + domain, requiring a small amount of bootstrapping work for each new domain. We evaluate + the efficacy of our framework on a task-oriented dialogue comprehension dataset, + MRCWOZ, which we curate by annotating questions for slots in the restaurant, taxi, + and hotel domains of the MultiWOZ 2.2 dataset (Zang et al., 2020). We run experiments + within a low-resource setting, where we pretrain a model on SQuAD, fine-tuning it + on either a small original data or on the synthetic data generated by our framework. + Velocidapter shows significant improvements using both the transformer-based BERTBase + and BiDAF as base models. We further show that the framework is easy to use by novice + users and conclude that Velocidapter can greatly help training over task-oriented + dialogues, especially for low-resourced emerging domains. +links: +- name: URL + url: https://aclanthology.org/2021.sigdial-1.14 +--- diff --git a/content/publication/aksu-etal-2022-n/cite.bib b/content/publication/aksu-etal-2022-n/cite.bib new file mode 100644 index 0000000..f789173 --- /dev/null +++ b/content/publication/aksu-etal-2022-n/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{aksu-etal-2022-n, + abstract = {Augmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure. In this work, we introduce an augmentation framework that utilizes belief state annotations to match turns from various dialogues and form new synthetic dialogues in a bottom-up manner. Unlike other augmentation strategies, it operates with as few as five examples. Our augmentation strategy yields significant improvements when both adapting a DST model to a new domain, and when adapting a language model to the DST task, on evaluations with TRADE and TOD-BERT models. Further analysis shows that our model performs better on seen values during training, and it is also more robust to unseen values. We conclude that exploiting belief state annotations enhances dialogue augmentation and results in improved models in n-shot training scenarios.}, + address = {Dublin, Ireland}, + author = {Aksu, Ibrahim and +Liu, Zhengyuan and +Kan, Min-Yen and +Chen, Nancy}, + booktitle = {Findings of the Association for Computational Linguistics: ACL 2022}, + doi = {10.18653/v1/2022.findings-acl.131}, + editor = {Muresan, Smaranda and +Nakov, Preslav and +Villavicencio, Aline}, + month = {May}, + pages = {1659--1671}, + publisher = {Association for Computational Linguistics}, + title = {N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking}, + url = {https://aclanthology.org/2022.findings-acl.131}, + year = {2022} +} diff --git a/content/publication/aksu-etal-2022-n/index.md b/content/publication/aksu-etal-2022-n/index.md new file mode 100644 index 0000000..30bb6fb --- /dev/null +++ b/content/publication/aksu-etal-2022-n/index.md @@ -0,0 +1,29 @@ +--- +title: N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking +authors: +- Ibrahim Aksu +- Zhengyuan Liu +- Min-Yen Kan +- Nancy Chen +date: '2022-05-01' +publishDate: '2024-07-05T17:09:42.588862Z' +publication_types: +- paper-conference +publication: '*Findings of the Association for Computational Linguistics: ACL 2022*' +doi: 10.18653/v1/2022.findings-acl.131 +abstract: Augmentation of task-oriented dialogues has followed standard methods used + for plain-text such as back-translation, word-level manipulation, and paraphrasing + despite its richly annotated structure. In this work, we introduce an augmentation + framework that utilizes belief state annotations to match turns from various dialogues + and form new synthetic dialogues in a bottom-up manner. Unlike other augmentation + strategies, it operates with as few as five examples. Our augmentation strategy + yields significant improvements when both adapting a DST model to a new domain, + and when adapting a language model to the DST task, on evaluations with TRADE and + TOD-BERT models. Further analysis shows that our model performs better on seen values + during training, and it is also more robust to unseen values. We conclude that exploiting + belief state annotations enhances dialogue augmentation and results in improved + models in n-shot training scenarios. +links: +- name: URL + url: https://aclanthology.org/2022.findings-acl.131 +--- diff --git a/content/publication/dou-etal-2022-towards/cite.bib b/content/publication/dou-etal-2022-towards/cite.bib new file mode 100644 index 0000000..b6f3706 --- /dev/null +++ b/content/publication/dou-etal-2022-towards/cite.bib @@ -0,0 +1,24 @@ +@inproceedings{dou-etal-2022-towards, + abstract = {In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by representing formulaic knowledge rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.}, + address = {Abu Dhabi, United Arab Emirates}, + author = {Dou, Longxu and +Gao, Yan and +Liu, Xuqi and +Pan, Mingyang and +Wang, Dingzirui and +Che, Wanxiang and +Zhan, Dechen and +Kan, Min-Yen and +Lou, Jian-Guang}, + booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, + doi = {10.18653/v1/2022.emnlp-main.350}, + editor = {Goldberg, Yoav and +Kozareva, Zornitsa and +Zhang, Yue}, + month = {December}, + pages = {5240--5253}, + publisher = {Association for Computational Linguistics}, + title = {Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge}, + url = {https://aclanthology.org/2022.emnlp-main.350}, + year = {2022} +} diff --git a/content/publication/dou-etal-2022-towards/index.md b/content/publication/dou-etal-2022-towards/index.md new file mode 100644 index 0000000..80ab2b5 --- /dev/null +++ b/content/publication/dou-etal-2022-towards/index.md @@ -0,0 +1,32 @@ +--- +title: Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge +authors: +- Longxu Dou +- Yan Gao +- Xuqi Liu +- Mingyang Pan +- Dingzirui Wang +- Wanxiang Che +- Dechen Zhan +- Min-Yen Kan +- Jian-Guang Lou +date: '2022-12-01' +publishDate: '2024-07-05T17:09:42.603419Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2022 Conference on Empirical Methods in Natural + Language Processing*' +doi: 10.18653/v1/2022.emnlp-main.350 +abstract: In this paper, we study the problem of knowledge-intensive text-to-SQL, + in which domain knowledge is necessary to parse expert questions into SQL queries + over domain-specific tables. We formalize this scenario by building a new benchmark + KnowSQL consisting of domain-specific questions covering various domains. We then + address this problem by representing formulaic knowledge rather than by annotating + additional data examples. More concretely, we construct a formulaic knowledge bank + as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic + knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% + improvement overall on KnowSQL. +links: +- name: URL + url: https://aclanthology.org/2022.emnlp-main.350 +--- diff --git a/content/publication/han-etal-2022-mm/cite.bib b/content/publication/han-etal-2022-mm/cite.bib new file mode 100644 index 0000000..3bebc61 --- /dev/null +++ b/content/publication/han-etal-2022-mm/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{han-etal-2022-mm, + abstract = {Existing multimodal tasks mostly target at the complete input modality setting, i.e., each modality is either complete or completely missing in both training and test sets. However, the randomly missing situations have still been underexplored. In this paper, we present a novel approach named MM-Align to address the missing-modality inference problem. Concretely, we propose 1) an alignment dynamics learning module based on the theory of optimal transport (OT) for missing data imputation; 2) a denoising training algorithm to enhance the quality of imputation as well as the accuracy of model predictions. Compared with previous generative methods which devote to restoring the missing inputs, MM-Align learns to capture and imitate the alignment dynamics between modality sequences. Results of comprehensive experiments on two multimodal tasks empirically demonstrate that our method can perform more accurate and faster inference and alleviate the overfitting issue under different missing conditions.}, + address = {Abu Dhabi, United Arab Emirates}, + author = {Han, Wei and +Chen, Hui and +Kan, Min-Yen and +Poria, Soujanya}, + booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, + doi = {10.18653/v1/2022.emnlp-main.717}, + editor = {Goldberg, Yoav and +Kozareva, Zornitsa and +Zhang, Yue}, + month = {December}, + pages = {10498--10511}, + publisher = {Association for Computational Linguistics}, + title = {MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences}, + url = {https://aclanthology.org/2022.emnlp-main.717}, + year = {2022} +} diff --git a/content/publication/han-etal-2022-mm/index.md b/content/publication/han-etal-2022-mm/index.md new file mode 100644 index 0000000..4f41024 --- /dev/null +++ b/content/publication/han-etal-2022-mm/index.md @@ -0,0 +1,32 @@ +--- +title: 'MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and + Accurate Inference on Missing Modality Sequences' +authors: +- Wei Han +- Hui Chen +- Min-Yen Kan +- Soujanya Poria +date: '2022-12-01' +publishDate: '2024-07-05T17:09:42.610472Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2022 Conference on Empirical Methods in Natural + Language Processing*' +doi: 10.18653/v1/2022.emnlp-main.717 +abstract: Existing multimodal tasks mostly target at the complete input modality setting, + i.e., each modality is either complete or completely missing in both training and + test sets. However, the randomly missing situations have still been underexplored. + In this paper, we present a novel approach named MM-Align to address the missing-modality + inference problem. Concretely, we propose 1) an alignment dynamics learning module + based on the theory of optimal transport (OT) for missing data imputation; 2) a + denoising training algorithm to enhance the quality of imputation as well as the + accuracy of model predictions. Compared with previous generative methods which devote + to restoring the missing inputs, MM-Align learns to capture and imitate the alignment + dynamics between modality sequences. Results of comprehensive experiments on two + multimodal tasks empirically demonstrate that our method can perform more accurate + and faster inference and alleviate the overfitting issue under different missing + conditions. +links: +- name: URL + url: https://aclanthology.org/2022.emnlp-main.717 +--- diff --git a/content/publication/han-etal-2024-self/cite.bib b/content/publication/han-etal-2024-self/cite.bib new file mode 100644 index 0000000..c2645d0 --- /dev/null +++ b/content/publication/han-etal-2024-self/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{han-etal-2024-self, + abstract = {Image--text models (ITMs) is the prevalent architecture to solve video question--answering tasks, which requires only a few input frames to save huge computational cost compared to video--language models.However, we find existent ITM video question--answering solutions either 1) adopt simplistic and unintentional sampling strategies, which may miss key frames to offer the answer clues; or 2) sample a large number of frames into divided groups, which the computational sources can not accommodate. In this work, we aim at an efficient sampling method towards the few-frame situations.We first summarize a family of prior sampling methods based on question--frame correlation into a unified one, dubbed *Most Implied Frames* (MIF). Through some primary results and analysis, Through analysis, we form a hypothesis that question-aware sampling is not necessary, from which we further propose the other method *Most Dominant Frames* (MDF).Experimental results on four public datasets and three advanced ITMs demonstrate that our proposed strategies can boost the performance for image--text pretrained models, and have a wide application scenario in terms of model architectures and dataset types. Our code is available at https://github.com/declare-lab/Sealingr̆lhttps://github.com/declare-lab/Sealing.}, + address = {Mexico City, Mexico}, + author = {Han, Wei and +Chen, Hui and +Kan, Min-Yen and +Poria, Soujanya}, + booktitle = {Findings of the Association for Computational Linguistics: NAACL 2024}, + editor = {Duh, Kevin and +Gomez, Helena and +Bethard, Steven}, + month = {June}, + pages = {2522--2534}, + publisher = {Association for Computational Linguistics}, + title = {Self-Adaptive Sampling for Accurate Video Question Answering on Image Text Models}, + url = {https://aclanthology.org/2024.findings-naacl.162}, + year = {2024} +} diff --git a/content/publication/han-etal-2024-self/index.md b/content/publication/han-etal-2024-self/index.md new file mode 100644 index 0000000..fbf6297 --- /dev/null +++ b/content/publication/han-etal-2024-self/index.md @@ -0,0 +1,32 @@ +--- +title: Self-Adaptive Sampling for Accurate Video Question Answering on Image Text + Models +authors: +- Wei Han +- Hui Chen +- Min-Yen Kan +- Soujanya Poria +date: '2024-06-01' +publishDate: '2024-07-05T17:09:42.578623Z' +publication_types: +- paper-conference +publication: '*Findings of the Association for Computational Linguistics: NAACL 2024*' +abstract: Image--text models (ITMs) is the prevalent architecture to solve video question--answering + tasks, which requires only a few input frames to save huge computational cost compared + to video--language models.However, we find existent ITM video question--answering + solutions either 1) adopt simplistic and unintentional sampling strategies, which + may miss key frames to offer the answer clues; or 2) sample a large number of frames + into divided groups, which the computational sources can not accommodate. In this + work, we aim at an efficient sampling method towards the few-frame situations.We + first summarize a family of prior sampling methods based on question--frame correlation + into a unified one, dubbed *Most Implied Frames* (MIF). Through some primary results + and analysis, Through analysis, we form a hypothesis that question-aware sampling + is not necessary, from which we further propose the other method *Most Dominant + Frames* (MDF).Experimental results on four public datasets and three advanced ITMs + demonstrate that our proposed strategies can boost the performance for image--text + pretrained models, and have a wide application scenario in terms of model architectures + and dataset types. Our code is available at https://github.com/declare-lab/Sealingr̆lhttps://github.com/declare-lab/Sealing. +links: +- name: URL + url: https://aclanthology.org/2024.findings-naacl.162 +--- diff --git a/content/publication/jain-etal-2022-comparative/cite.bib b/content/publication/jain-etal-2022-comparative/cite.bib new file mode 100644 index 0000000..d26e042 --- /dev/null +++ b/content/publication/jain-etal-2022-comparative/cite.bib @@ -0,0 +1,21 @@ +@inproceedings{jain-etal-2022-comparative, + abstract = {We model products′ reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generation from contrasting opinions regarding a product, and an analysis of performance of a pre-trained BERT model to generate such snippets.}, + address = {Dublin, Ireland}, + author = {Jain, Saurabh and +Miao, Yisong and +Kan, Min-Yen}, + booktitle = {Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)}, + doi = {10.18653/v1/2022.ecnlp-1.7}, + editor = {Malmasi, Shervin and +Rokhlenko, Oleg and +Ueffing, Nicola and +Guy, Ido and +Agichtein, Eugene and +Kallumadi, Surya}, + month = {May}, + pages = {49--57}, + publisher = {Association for Computational Linguistics}, + title = {Comparative Snippet Generation}, + url = {https://aclanthology.org/2022.ecnlp-1.7}, + year = {2022} +} diff --git a/content/publication/jain-etal-2022-comparative/index.md b/content/publication/jain-etal-2022-comparative/index.md new file mode 100644 index 0000000..b4df4e5 --- /dev/null +++ b/content/publication/jain-etal-2022-comparative/index.md @@ -0,0 +1,22 @@ +--- +title: Comparative Snippet Generation +authors: +- Saurabh Jain +- Yisong Miao +- Min-Yen Kan +date: '2022-05-01' +publishDate: '2024-07-05T17:09:42.617512Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)*' +doi: 10.18653/v1/2022.ecnlp-1.7 +abstract: We model products′ reviews to generate comparative responses consisting + of positive and negative experiences regarding the product. Specifically, we generate + a single-sentence, comparative response from a given positive and a negative opinion. + We contribute the first dataset for this task of Comparative Snippet Generation + from contrasting opinions regarding a product, and an analysis of performance of + a pre-trained BERT model to generate such snippets. +links: +- name: URL + url: https://aclanthology.org/2022.ecnlp-1.7 +--- diff --git a/content/publication/qin-etal-2022-gl/cite.bib b/content/publication/qin-etal-2022-gl/cite.bib new file mode 100644 index 0000000..d53dffd --- /dev/null +++ b/content/publication/qin-etal-2022-gl/cite.bib @@ -0,0 +1,22 @@ +@inproceedings{qin-etal-2022-gl, + abstract = {Due to high data demands of current methods, attention to zero-shot cross-lingual spoken language understanding (SLU) has grown, as such approaches greatly reduce human annotation effort. However, existing models solely rely on shared parameters, which can only perform implicit alignment across languages. We present Global-Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming. Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages. In addition, a key step in GL-CLeF is a proposed Local and Global component, which achieves a fine-grained cross-lingual transfer (i.e., sentence-level Local intent transfer, token-level Local slot transfer, and semantic-level Global transfer across intent and slot). Experiments on MultiATIS++ show that GL-CLeF achieves the best performance and successfully pulls representations of similar sentences across languages closer.}, + address = {Dublin, Ireland}, + author = {Qin, Libo and +Chen, Qiguang and +Xie, Tianbao and +Li, Qixin and +Lou, Jian-Guang and +Che, Wanxiang and +Kan, Min-Yen}, + booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/2022.acl-long.191}, + editor = {Muresan, Smaranda and +Nakov, Preslav and +Villavicencio, Aline}, + month = {May}, + pages = {2677--2686}, + publisher = {Association for Computational Linguistics}, + title = {GL-CLeF: A Global--Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding}, + url = {https://aclanthology.org/2022.acl-long.191}, + year = {2022} +} diff --git a/content/publication/qin-etal-2022-gl/index.md b/content/publication/qin-etal-2022-gl/index.md new file mode 100644 index 0000000..aa0e363 --- /dev/null +++ b/content/publication/qin-etal-2022-gl/index.md @@ -0,0 +1,36 @@ +--- +title: 'GL-CLeF: A Global--Local Contrastive Learning Framework for Cross-lingual + Spoken Language Understanding' +authors: +- Libo Qin +- Qiguang Chen +- Tianbao Xie +- Qixin Li +- Jian-Guang Lou +- Wanxiang Che +- Min-Yen Kan +date: '2022-05-01' +publishDate: '2024-07-05T17:09:42.638128Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 60th Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/2022.acl-long.191 +abstract: Due to high data demands of current methods, attention to zero-shot cross-lingual + spoken language understanding (SLU) has grown, as such approaches greatly reduce + human annotation effort. However, existing models solely rely on shared parameters, + which can only perform implicit alignment across languages. We present Global-Local + Contrastive Learning Framework (GL-CLeF) to address this shortcoming. Specifically, + we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual + views of the same utterance, then encourage their representations to be more similar + than negative example pairs, which achieves to explicitly align representations + of similar sentences across languages. In addition, a key step in GL-CLeF is a proposed + Local and Global component, which achieves a fine-grained cross-lingual transfer + (i.e., sentence-level Local intent transfer, token-level Local slot transfer, and + semantic-level Global transfer across intent and slot). Experiments on MultiATIS++ + show that GL-CLeF achieves the best performance and successfully pulls representations + of similar sentences across languages closer. +links: +- name: URL + url: https://aclanthology.org/2022.acl-long.191 +--- diff --git a/content/publication/ramesh-kashyap-etal-2022-different/cite.bib b/content/publication/ramesh-kashyap-etal-2022-different/cite.bib new file mode 100644 index 0000000..ed49be6 --- /dev/null +++ b/content/publication/ramesh-kashyap-etal-2022-different/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{ramesh-kashyap-etal-2022-different, + abstract = {Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content while adapting to the target domain. However, it does not explicitly maintain other attributes between the source and translated text: e.g., text length and descriptiveness. Maintaining constraints in transfer has several downstream applications, including data augmentation and debiasing. We introduce a method for such constrained unsupervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooperate and reduce the same loss. The first is a contrastive loss and the second is a classification loss --- aiming to regularize the latent space further and bring similar sentences closer together. We demonstrate that such training retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures.}, + address = {Dublin, Ireland}, + author = {Ramesh Kashyap, Abhinav and +Hazarika, Devamanyu and +Kan, Min-Yen and +Zimmermann, Roger and +Poria, Soujanya}, + booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/2022.acl-long.32}, + editor = {Muresan, Smaranda and +Nakov, Preslav and +Villavicencio, Aline}, + month = {May}, + pages = {416--431}, + publisher = {Association for Computational Linguistics}, + title = {So Different Yet So Alike! Constrained Unsupervised Text Style Transfer}, + url = {https://aclanthology.org/2022.acl-long.32}, + year = {2022} +} diff --git a/content/publication/ramesh-kashyap-etal-2022-different/index.md b/content/publication/ramesh-kashyap-etal-2022-different/index.md new file mode 100644 index 0000000..a29d15a --- /dev/null +++ b/content/publication/ramesh-kashyap-etal-2022-different/index.md @@ -0,0 +1,34 @@ +--- +title: So Different Yet So Alike! Constrained Unsupervised Text Style Transfer +authors: +- Abhinav Ramesh Kashyap +- Devamanyu Hazarika +- Min-Yen Kan +- Roger Zimmermann +- Soujanya Poria +date: '2022-05-01' +publishDate: '2024-07-05T17:09:42.630957Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 60th Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/2022.acl-long.32 +abstract: 'Automatic transfer of text between domains has become popular in recent + times. One of its aims is to preserve the semantic content while adapting to the + target domain. However, it does not explicitly maintain other attributes between + the source and translated text: e.g., text length and descriptiveness. Maintaining + constraints in transfer has several downstream applications, including data augmentation + and debiasing. We introduce a method for such constrained unsupervised text style + transfer by introducing two complementary losses to the generative adversarial network + (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative + losses where the discriminator and the generator cooperate and reduce the same loss. + The first is a contrastive loss and the second is a classification loss --- aiming + to regularize the latent space further and bring similar sentences closer together. + We demonstrate that such training retains lexical, syntactic and domain-specific + constraints between domains for multiple benchmark datasets, including ones where + more than one attribute change. We show that the complementary cooperative losses + improve text quality, according to both automated and human evaluation measures.' +links: +- name: URL + url: https://aclanthology.org/2022.acl-long.32 +--- diff --git a/content/publication/xu-etal-2022-corefdiffs/cite.bib b/content/publication/xu-etal-2022-corefdiffs/cite.bib new file mode 100644 index 0000000..8281f61 --- /dev/null +++ b/content/publication/xu-etal-2022-corefdiffs/cite.bib @@ -0,0 +1,36 @@ +@inproceedings{xu-etal-2022-corefdiffs, + abstract = {Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation.}, + address = {Gyeongju, Republic of Korea}, + author = {Xu, Lin and +Zhou, Qixian and +Fu, Jinlan and +Kan, Min-Yen and +Ng, See-Kiong}, + booktitle = {Proceedings of the 29th International Conference on Computational Linguistics}, + editor = {Calzolari, Nicoletta and +Huang, Chu-Ren and +Kim, Hansaem and +Pustejovsky, James and +Wanner, Leo and +Choi, Key-Sun and +Ryu, Pum-Mo and +Chen, Hsin-Hsi and +Donatelli, Lucia and +Ji, Heng and +Kurohashi, Sadao and +Paggio, Patrizia and +Xue, Nianwen and +Kim, Seokhwan and +Hahm, Younggyun and +He, Zhong and +Lee, Tony Kyungil and +Santus, Enrico and +Bond, Francis and +Na, Seung-Hoon}, + month = {October}, + pages = {471--484}, + publisher = {International Committee on Computational Linguistics}, + title = {CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations}, + url = {https://aclanthology.org/2022.coling-1.38}, + year = {2022} +} diff --git a/content/publication/xu-etal-2022-corefdiffs/index.md b/content/publication/xu-etal-2022-corefdiffs/index.md new file mode 100644 index 0000000..1cc91ee --- /dev/null +++ b/content/publication/xu-etal-2022-corefdiffs/index.md @@ -0,0 +1,31 @@ +--- +title: 'CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded + Conversations' +authors: +- Lin Xu +- Qixian Zhou +- Jinlan Fu +- Min-Yen Kan +- See-Kiong Ng +date: '2022-10-01' +publishDate: '2024-07-05T17:09:42.624059Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 29th International Conference on Computational Linguistics*' +abstract: Knowledge-grounded dialog systems need to incorporate smooth transitions + among knowledge selected for generating responses, to ensure that dialog flows naturally. + For document-grounded dialog systems, the inter- and intra-document knowledge relations + can be used to model such conversational flows. We develop a novel Multi-Document + Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships + based on commonsense and similarity and the intra-document co-referential structures + of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential + and Differential flow management method, to linearize the static Coref-MDG into + conversational sequence logic. CorefDiffs performs knowledge selection by accounting + for contextual graph structures and the knowledge difference sequences. CorefDiffs + significantly outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public + benchmarks. This demonstrates that the effective modeling of co-reference and knowledge + difference for dialog flows are critical for transitions in document-grounded conversation. +links: +- name: URL + url: https://aclanthology.org/2022.coling-1.38 +--- diff --git a/content/publication/zhang-etal-2022-interpreting/cite.bib b/content/publication/zhang-etal-2022-interpreting/cite.bib new file mode 100644 index 0000000..dc902fb --- /dev/null +++ b/content/publication/zhang-etal-2022-interpreting/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{zhang-etal-2022-interpreting, + abstract = {Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear why models are less robust to some perturbations than others. In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence). We further give a causal justification for the learnability metric. We conduct extensive experiments with four prominent NLP models --- TextRNN, BERT, RoBERTa and XLNet --- over eight types of textual perturbations on three datasets. We show that a model which is better at identifying a perturbation (higher learnability) becomes worse at ignoring such a perturbation at test time (lower robustness), providing empirical support for our hypothesis.}, + address = {Dublin, Ireland}, + author = {Zhang, Yunxiang and +Pan, Liangming and +Tan, Samson and +Kan, Min-Yen}, + booktitle = {Findings of the Association for Computational Linguistics: ACL 2022}, + doi = {10.18653/v1/2022.findings-acl.315}, + editor = {Muresan, Smaranda and +Nakov, Preslav and +Villavicencio, Aline}, + month = {May}, + pages = {3993--4007}, + publisher = {Association for Computational Linguistics}, + title = {Interpreting the Robustness of Neural NLP Models to Textual Perturbations}, + url = {https://aclanthology.org/2022.findings-acl.315}, + year = {2022} +} diff --git a/content/publication/zhang-etal-2022-interpreting/index.md b/content/publication/zhang-etal-2022-interpreting/index.md new file mode 100644 index 0000000..f5534f5 --- /dev/null +++ b/content/publication/zhang-etal-2022-interpreting/index.md @@ -0,0 +1,29 @@ +--- +title: Interpreting the Robustness of Neural NLP Models to Textual Perturbations +authors: +- Yunxiang Zhang +- Liangming Pan +- Samson Tan +- Min-Yen Kan +date: '2022-05-01' +publishDate: '2024-07-05T17:09:42.596004Z' +publication_types: +- paper-conference +publication: '*Findings of the Association for Computational Linguistics: ACL 2022*' +doi: 10.18653/v1/2022.findings-acl.315 +abstract: Modern Natural Language Processing (NLP) models are known to be sensitive + to input perturbations and their performance can decrease when applied to real-world, + noisy data. However, it is still unclear why models are less robust to some perturbations + than others. In this work, we test the hypothesis that the extent to which a model + is affected by an unseen textual perturbation (robustness) can be explained by the + learnability of the perturbation (defined as how well the model learns to identify + the perturbation with a small amount of evidence). We further give a causal justification + for the learnability metric. We conduct extensive experiments with four prominent + NLP models --- TextRNN, BERT, RoBERTa and XLNet --- over eight types of textual + perturbations on three datasets. We show that a model which is better at identifying + a perturbation (higher learnability) becomes worse at ignoring such a perturbation + at test time (lower robustness), providing empirical support for our hypothesis. +links: +- name: URL + url: https://aclanthology.org/2022.findings-acl.315 +--- From dedf3aa681e37365a4566f3c7b1e1e942733d1e4 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Sat, 6 Jul 2024 02:22:05 +0000 Subject: [PATCH 07/22] Ingesting more publications --- .../aksu-etal-2021-velocidapter/index.md | 2 +- .../dou-etal-2022-towards/index.md | 2 +- content/publication/han-etal-2022-mm/index.md | 2 +- .../publication/han-etal-2024-self/index.md | 2 +- .../huang-etal-2022-lightweight/index.md | 2 +- .../jain-etal-2022-comparative/index.md | 2 +- publications.bib | 538 +++++++++++++----- 7 files changed, 392 insertions(+), 158 deletions(-) diff --git a/content/publication/aksu-etal-2021-velocidapter/index.md b/content/publication/aksu-etal-2021-velocidapter/index.md index 82f7444..f1cbd94 100644 --- a/content/publication/aksu-etal-2021-velocidapter/index.md +++ b/content/publication/aksu-etal-2021-velocidapter/index.md @@ -4,7 +4,7 @@ title: 'Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synt authors: - Ibrahim Taha Aksu - Zhengyuan Liu -- Min-Yen Kan +- min - Nancy Chen date: '2021-07-01' publishDate: '2024-07-05T17:09:42.645613Z' diff --git a/content/publication/dou-etal-2022-towards/index.md b/content/publication/dou-etal-2022-towards/index.md index 80ab2b5..7a92e1d 100644 --- a/content/publication/dou-etal-2022-towards/index.md +++ b/content/publication/dou-etal-2022-towards/index.md @@ -8,7 +8,7 @@ authors: - Dingzirui Wang - Wanxiang Che - Dechen Zhan -- Min-Yen Kan +- min - Jian-Guang Lou date: '2022-12-01' publishDate: '2024-07-05T17:09:42.603419Z' diff --git a/content/publication/han-etal-2022-mm/index.md b/content/publication/han-etal-2022-mm/index.md index 4f41024..9cb0ca8 100644 --- a/content/publication/han-etal-2022-mm/index.md +++ b/content/publication/han-etal-2022-mm/index.md @@ -4,7 +4,7 @@ title: 'MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast a authors: - Wei Han - Hui Chen -- Min-Yen Kan +- min - Soujanya Poria date: '2022-12-01' publishDate: '2024-07-05T17:09:42.610472Z' diff --git a/content/publication/han-etal-2024-self/index.md b/content/publication/han-etal-2024-self/index.md index fbf6297..0688a16 100644 --- a/content/publication/han-etal-2024-self/index.md +++ b/content/publication/han-etal-2024-self/index.md @@ -4,7 +4,7 @@ title: Self-Adaptive Sampling for Accurate Video Question Answering on Image Tex authors: - Wei Han - Hui Chen -- Min-Yen Kan +- min - Soujanya Poria date: '2024-06-01' publishDate: '2024-07-05T17:09:42.578623Z' diff --git a/content/publication/huang-etal-2022-lightweight/index.md b/content/publication/huang-etal-2022-lightweight/index.md index d0efc15..a456c54 100644 --- a/content/publication/huang-etal-2022-lightweight/index.md +++ b/content/publication/huang-etal-2022-lightweight/index.md @@ -5,7 +5,7 @@ authors: - Abhinav Ramesh Kashyap - Yanxia Qin - Yajing Yang -- Min-Yen Kan +- min date: '2022-10-01' publishDate: '2024-07-05T10:15:26.841390Z' publication_types: diff --git a/content/publication/jain-etal-2022-comparative/index.md b/content/publication/jain-etal-2022-comparative/index.md index b4df4e5..cb6c493 100644 --- a/content/publication/jain-etal-2022-comparative/index.md +++ b/content/publication/jain-etal-2022-comparative/index.md @@ -3,7 +3,7 @@ title: Comparative Snippet Generation authors: - Saurabh Jain - Yisong Miao -- Min-Yen Kan +- min date: '2022-05-01' publishDate: '2024-07-05T17:09:42.617512Z' publication_types: diff --git a/publications.bib b/publications.bib index f1de23a..5081b98 100644 --- a/publications.bib +++ b/publications.bib @@ -17,111 +17,417 @@ @inproceedings{han-etal-2024-self abstract = "Image{--}text models (ITMs) is the prevalent architecture to solve video question{--}answering tasks, which requires only a few input frames to save huge computational cost compared to video{--}language models.However, we find existent ITM video question{--}answering solutions either 1) adopt simplistic and unintentional sampling strategies, which may miss key frames to offer the answer clues; or 2) sample a large number of frames into divided groups, which the computational sources can not accommodate. In this work, we aim at an efficient sampling method towards the few-frame situations.We first summarize a family of prior sampling methods based on question{--}frame correlation into a unified one, dubbed *Most Implied Frames* (MIF). Through some primary results and analysis, Through analysis, we form a hypothesis that question-aware sampling is not necessary, from which we further propose the other method *Most Dominant Frames* (MDF).Experimental results on four public datasets and three advanced ITMs demonstrate that our proposed strategies can boost the performance for image{--}text pretrained models, and have a wide application scenario in terms of model architectures and dataset types. Our code is available at https://github.com/declare-lab/Sealing\url{https://github.com/declare-lab/Sealing}.", } -@inproceedings{aksu-etal-2022-n, - title = "N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking", - author = "Aksu, Ibrahim and - Liu, Zhengyuan and +@inproceedings{li-etal-2024-uno, + title = "{UNO}-{DST}: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking", + author = "Li, Chuang and + Zhang, Yan and Kan, Min-Yen and - Chen, Nancy", - editor = "Muresan, Smaranda and - Nakov, Preslav and - Villavicencio, Aline", - booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", - month = may, - year = "2022", - address = "Dublin, Ireland", + Li, Haizhou", + editor = "Duh, Kevin and + Gomez, Helena and + Bethard, Steven", + booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024", + month = jun, + year = "2024", + address = "Mexico City, Mexico", publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.findings-acl.131", - doi = "10.18653/v1/2022.findings-acl.131", - pages = "1659--1671", - abstract = "Augmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure. In this work, we introduce an augmentation framework that utilizes belief state annotations to match turns from various dialogues and form new synthetic dialogues in a bottom-up manner. Unlike other augmentation strategies, it operates with as few as five examples. Our augmentation strategy yields significant improvements when both adapting a DST model to a new domain, and when adapting a language model to the DST task, on evaluations with TRADE and TOD-BERT models. Further analysis shows that our model performs better on seen values during training, and it is also more robust to unseen values. We conclude that exploiting belief state annotations enhances dialogue augmentation and results in improved models in n-shot training scenarios.", + url = "https://aclanthology.org/2024.findings-naacl.187", + pages = "2972--2983", + abstract = "Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, but ignore unlabelled data in the target domain.We transform zero-shot DST into few-shot DST by utilising such unlabelled data via joint and self-training methods. Our method incorporates auxiliary tasks that generate slot types as inverse prompts for main tasks, creating slot values during joint training. Cycle consistency between these two tasks enables the generation and selection of quality samples in unknown target domains for subsequent fine-tuning. This approach also facilitates automatic label creation, thereby optimizing the training and fine-tuning of DST models. We demonstrate this method{'}s effectiveness on general language models in zero-shot scenarios, improving average joint goal accuracy by 8{\%} across all domains in MultiWOZ.", } -@inproceedings{zhang-etal-2022-interpreting, - title = "Interpreting the Robustness of Neural {NLP} Models to Textual Perturbations", - author = "Zhang, Yunxiang and +@inproceedings{zhang-etal-2024-nnose, + title = "{NNOSE}: Nearest Neighbor Occupational Skill Extraction", + author = "Zhang, Mike and + van der Goot, Rob and + Kan, Min-Yen and + Plank, Barbara", + editor = "Graham, Yvette and + Purver, Matthew", + booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = mar, + year = "2024", + address = "St. Julian{'}s, Malta", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2024.eacl-long.35", + pages = "589--608", + abstract = "The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job description datasets, there is a need for systems that handle their diversity well. We tackle the complexity in occupational skill datasets tasks{---}combining and leveraging multiple datasets for skill extraction, to identify rarely observed skills within a dataset, and overcoming the scarcity of skills across datasets. In particular, we investigate the retrieval-augmentation of language models, employing an external datastore for retrieving similar skills in a dataset-unifying manner. Our proposed method, \textbf{N}earest \textbf{N}eighbor \textbf{O}ccupational \textbf{S}kill \textbf{E}xtraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring skills from other datasets in the datastore. This improves skill extraction \textit{without} additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30{\%} span-F1 in cross-dataset settings.", +} + +@inproceedings{meng-etal-2023-followupqg, + title = "{F}ollowup{QG}: Towards information-seeking follow-up question generation", + author = "Meng, Yan and Pan, Liangming and - Tan, Samson and + Cao, Yixin and Kan, Min-Yen", - editor = "Muresan, Smaranda and + editor = "Park, Jong C. and + Arase, Yuki and + Hu, Baotian and + Lu, Wei and + Wijaya, Derry and + Purwarianti, Ayu and + Krisnadhi, Adila Alfa", + booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = nov, + year = "2023", + address = "Nusa Dua, Bali", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.ijcnlp-main.17", + doi = "10.18653/v1/2023.ijcnlp-main.17", + pages = "252--271", +} + +@inproceedings{pan-etal-2023-investigating, + title = "Investigating Zero- and Few-shot Generalization in Fact Verification", + author = "Pan, Liangming and + Zhang, Yunxiang and + Kan, Min-Yen", + editor = "Park, Jong C. and + Arase, Yuki and + Hu, Baotian and + Lu, Wei and + Wijaya, Derry and + Purwarianti, Ayu and + Krisnadhi, Adila Alfa", + booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = nov, + year = "2023", + address = "Nusa Dua, Bali", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.ijcnlp-main.34", + doi = "10.18653/v1/2023.ijcnlp-main.34", + pages = "511--524", +} +@inproceedings{pan-etal-2023-attacking, + title = "Attacking Open-domain Question Answering by Injecting Misinformation", + author = "Pan, Liangming and + Chen, Wenhu and + Kan, Min-Yen and + Wang, William Yang", + editor = "Park, Jong C. and + Arase, Yuki and + Hu, Baotian and + Lu, Wei and + Wijaya, Derry and + Purwarianti, Ayu and + Krisnadhi, Adila Alfa", + booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = nov, + year = "2023", + address = "Nusa Dua, Bali", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.ijcnlp-main.35", + doi = "10.18653/v1/2023.ijcnlp-main.35", + pages = "525--539", +} + +@inproceedings{pan-etal-2023-risk, + title = "On the Risk of Misinformation Pollution with Large Language Models", + author = "Pan, Yikang and + Pan, Liangming and + Chen, Wenhu and Nakov, Preslav and - Villavicencio, Aline", - booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", - month = may, - year = "2022", - address = "Dublin, Ireland", + Kan, Min-Yen and + Wang, William", + editor = "Bouamor, Houda and + Pino, Juan and + Bali, Kalika", + booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023", + month = dec, + year = "2023", + address = "Singapore", publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.findings-acl.315", - doi = "10.18653/v1/2022.findings-acl.315", - pages = "3993--4007", - abstract = "Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear why models are less robust to some perturbations than others. In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence). We further give a causal justification for the learnability metric. We conduct extensive experiments with four prominent NLP models {---} TextRNN, BERT, RoBERTa and XLNet {---} over eight types of textual perturbations on three datasets. We show that a model which is better at identifying a perturbation (higher learnability) becomes worse at ignoring such a perturbation at test time (lower robustness), providing empirical support for our hypothesis.", + url = "https://aclanthology.org/2023.findings-emnlp.97", + doi = "10.18653/v1/2023.findings-emnlp.97", + pages = "1389--1403", + abstract = "We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems. We establish a threat model and simulate potential misuse scenarios, both unintentional and intentional, to assess the extent to which LLMs can be utilized to produce misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation (up to 87{\%}) in the performance of ODQA systems. Moreover, we uncover disparities in the attributes associated with persuading humans and machines, presenting an obstacle to current human-centric approaches to combat misinformation. To mitigate the harm caused by LLM-generated misinformation, we propose three defense strategies: misinformation detection, vigilant prompting, and reader ensemble. These approaches have demonstrated promising results, albeit with certain associated costs. Lastly, we discuss the practicality of utilizing LLMs as automatic misinformation generators and provide relevant resources and code to facilitate future research in this area.", +} + +@inproceedings{xie-etal-2023-echo, + title = "{ECH}o: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning", + author = "Xie, Yuxi and + Li, Guanzhen and + Kan, Min-Yen", + editor = "Bouamor, Houda and + Pino, Juan and + Bali, Kalika", + booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023", + month = dec, + year = "2023", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.findings-emnlp.268", + doi = "10.18653/v1/2023.findings-emnlp.268", + pages = "4064--4085", + abstract = "We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios. ECHo employs real-world human-centric deductive information building on a television crime drama. ECHo requires the Theory-of-Mind (ToM) ability to understand and reason about social interactions based on multimodal information. Using ECHo, we propose a unified Chain-of-Thought (CoT) framework to assess the reasoning capability of current AI systems. Our ToM-enhanced CoT pipeline accommodates various large foundation models in both zero-shot and few-shot visio-linguistic reasoning. We use this framework to scrutinize recent large foundation models such as InstructGPT and MiniGPT-4 on three diagnostic human-centric tasks. Further analysis demonstrates ECHo as a challenging dataset to expose imperfections and inconsistencies in reasoning. Our data and code are publicly available at [https://github.com/YuxiXie/ECHo](https://github.com/YuxiXie/ECHo).", } -@inproceedings{dou-etal-2022-towards, - title = "Towards Knowledge-Intensive Text-to-{SQL} Semantic Parsing with Formulaic Knowledge", - author = "Dou, Longxu and - Gao, Yan and - Liu, Xuqi and - Pan, Mingyang and - Wang, Dingzirui and - Che, Wanxiang and - Zhan, Dechen and +@inproceedings{li-etal-2023-coannotating, + title = "{C}o{A}nnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation", + author = "Li, Minzhi and + Shi, Taiwei and + Ziems, Caleb and Kan, Min-Yen and - Lou, Jian-Guang", - editor = "Goldberg, Yoav and - Kozareva, Zornitsa and - Zhang, Yue", - booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", + Chen, Nancy and + Liu, Zhengyuan and + Yang, Diyi", + editor = "Bouamor, Houda and + Pino, Juan and + Bali, Kalika", + booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, - year = "2022", - address = "Abu Dhabi, United Arab Emirates", + year = "2023", + address = "Singapore", publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.emnlp-main.350", - doi = "10.18653/v1/2022.emnlp-main.350", - pages = "5240--5253", - abstract = "In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by representing formulaic knowledge rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2{\%} improvement overall on KnowSQL.", + url = "https://aclanthology.org/2023.emnlp-main.92", + doi = "10.18653/v1/2023.emnlp-main.92", + pages = "1487--1505", + abstract = "Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many text-annotation tasks, comparable with or even exceeding human annotators. Such LLMs can serve as alternatives for manual annotation, due to lower costs and higher scalability. However, limited work has leveraged LLMs as complementary annotators, nor explored how annotation work is best allocated among humans and LLMs to achieve both quality and cost objectives. We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Under this framework, we utilize uncertainty to estimate LLMs{'} annotation capability. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21{\%} performance improvement over random baseline. For code implementation, see https://github.com/SALT-NLP/CoAnnotating.", } -@inproceedings{han-etal-2022-mm, - title = "{MM}-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences", - author = "Han, Wei and - Chen, Hui and +@inproceedings{lu-etal-2023-scitab, + title = "{SCITAB}: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables", + author = "Lu, Xinyuan and + Pan, Liangming and + Liu, Qian and + Nakov, Preslav and + Kan, Min-Yen", + editor = "Bouamor, Houda and + Pino, Juan and + Bali, Kalika", + booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", + month = dec, + year = "2023", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.emnlp-main.483", + doi = "10.18653/v1/2023.emnlp-main.483", + pages = "7787--7813", + abstract = "Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence. We present SCITAB, a challenging evaluation dataset consisting of 1.2K expert-verified scientific claims that 1) originate from authentic scientific publications and 2) require compositional reasoning for verification. The claims are paired with evidence-containing scientific tables annotated with labels. Through extensive evaluations, we demonstrate that SCITAB poses a significant challenge to state-of-the-art models, including table-based pretraining models and large language models. All models except GPT-4 achieved performance barely above random guessing. Popular prompting techniques, such as Chain-of-Thought, do not achieve much performance gains on SCITAB. Our analysis uncovers several unique challenges posed by SCITAB, including table grounding, claim ambiguity, and compositional reasoning. Our codes and data are publicly available at https://github.com/XinyuanLu00/SciTab.", +} + +@inproceedings{rohatgi-etal-2023-acl, + title = "The {ACL} {OCL} Corpus: Advancing Open Science in Computational Linguistics", + author = "Rohatgi, Shaurya and + Qin, Yanxia and + Aw, Benjamin and + Unnithan, Niranjana and + Kan, Min-Yen", + editor = "Bouamor, Houda and + Pino, Juan and + Bali, Kalika", + booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", + month = dec, + year = "2023", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.emnlp-main.640", + doi = "10.18653/v1/2023.emnlp-main.640", + pages = "10348--10361", + abstract = "We present ACL OCL, a scholarly corpus derived from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain. Integrating and enhancing the previous versions of the ACL Anthology, the ACL OCL contributes metadata, PDF files, citation graphs and additional structured full texts with sections, figures, and links to a large knowledge resource (Semantic Scholar). The ACL OCL spans seven decades, containing 73K papers, alongside 210K figures. We spotlight how ACL OCL applies to observe trends in computational linguistics. By detecting paper topics with a supervised neural model, we note that interest in {``}Syntax: Tagging, Chunking and Parsing{''} is waning and {``}Natural Language Generation{''} is resurging. Our dataset is available from HuggingFace (https://huggingface.co/datasets/WINGNUS/ACL-OCL).", +} + + +@inproceedings{diao-etal-2023-doolittle, + title = "Doolittle: Benchmarks and Corpora for Academic Writing Formalization", + author = "Diao, Shizhe and + Lei, Yongyu and + Pan, Liangming and + Fang, Tianqing and + Zhou, Wangchunshu and + Keh, Sedrick and Kan, Min-Yen and - Poria, Soujanya", - editor = "Goldberg, Yoav and - Kozareva, Zornitsa and - Zhang, Yue", - booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", + Zhang, Tong", + editor = "Bouamor, Houda and + Pino, Juan and + Bali, Kalika", + booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, - year = "2022", - address = "Abu Dhabi, United Arab Emirates", + year = "2023", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.emnlp-main.809", + doi = "10.18653/v1/2023.emnlp-main.809", + pages = "13093--13111", + abstract = "Improving the quality of academic writing is a meaningful but challenging task. Conventional methods of language refinement focus on narrow, specific linguistic features within isolated sentences, such as grammatical errors and improper word use. We propose a more general task, Academic Writing Formalization (AWF), to improve the overall quality of formal academic writing at the paragraph level. We formulate this language refinement task as a formal text style transfer task which transfers informal-academic text to formal-academic and contribute a large-scale non-parallel dataset, Doolittle, for this purpose. Concurrently, we apply a method named metric-oriented reinforcement learning (MORL) to two large language models (LLM) where we incorporate different levels of automatic feedback into the training process. Our experiments reveal that existing text transfer models and grammatical error correction models address certain aspects of AWF but still have a significant performance gap compared to human performance. Meanwhile, language models fine-tuned with our MORL method exhibit considerably improved performance, rivaling the latest chatbot ChatGPT, but still have a non-negligible gap compared to the ground truth formal-academic texts in Doolittle.", +} + + +@inproceedings{pan-etal-2023-qacheck, + title = "{QAC}heck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking", + author = "Pan, Liangming and + Lu, Xinyuan and + Kan, Min-Yen and + Nakov, Preslav", + editor = "Feng, Yansong and + Lefever, Els", + booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = dec, + year = "2023", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.emnlp-demo.23", + doi = "10.18653/v1/2023.emnlp-demo.23", + pages = "264--273", + abstract = "Fact-checking real-world claims often requires intricate, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking systems often lack transparency in their decision-making, making it challenging for users to comprehend their reasoning process. To address this, we propose the Question-guided Multi-hop Fact-Checking (QACheck) system, which guides the model{'}s reasoning process by asking a series of questions critical for verifying a claim. QACheck has five key modules: a claim verifier, a question generator, a question-answering module, a QA validator, and a reasoner. Users can input a claim into QACheck, which then predicts its veracity and provides a comprehensive report detailing its reasoning process, guided by a sequence of (question, answer) pairs. QACheck also provides the source of evidence supporting each question, fostering a transparent, explainable, and user-friendly fact-checking process.", +} + +@inproceedings{ding-etal-2023-cocoscisum, + title = "{C}oco{S}ci{S}um: A Scientific Summarization Toolkit with Compositional Controllability", + author = "Ding, Yixi and + Qin, Yanxia and + Liu, Qian and + Kan, Min-Yen", + editor = "Feng, Yansong and + Lefever, Els", + booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = dec, + year = "2023", + address = "Singapore", publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.emnlp-main.717", - doi = "10.18653/v1/2022.emnlp-main.717", - pages = "10498--10511", - abstract = "Existing multimodal tasks mostly target at the complete input modality setting, i.e., each modality is either complete or completely missing in both training and test sets. However, the randomly missing situations have still been underexplored. In this paper, we present a novel approach named MM-Align to address the missing-modality inference problem. Concretely, we propose 1) an alignment dynamics learning module based on the theory of optimal transport (OT) for missing data imputation; 2) a denoising training algorithm to enhance the quality of imputation as well as the accuracy of model predictions. Compared with previous generative methods which devote to restoring the missing inputs, MM-Align learns to capture and imitate the alignment dynamics between modality sequences. Results of comprehensive experiments on two multimodal tasks empirically demonstrate that our method can perform more accurate and faster inference and alleviate the overfitting issue under different missing conditions.", + url = "https://aclanthology.org/2023.emnlp-demo.47", + doi = "10.18653/v1/2023.emnlp-demo.47", + pages = "518--526", + abstract = "We present a novel toolkit for controlled summarization of scientific documents, designed for the specific needs of the scientific community. Our system generates summaries based on user preferences, adjusting key attributes specifically of length and keyword inclusion. A distinguishing feature is its ability to manage multiple attributes concurrently, demonstrating Compositional Controllability for Scientific Summarization (CocoSciSum). Benchmarked against the strong Flan-T5 baseline, CocoSciSum exhibits superior performance on both the quality of summaries generated and the control over single and multiple attributes. Moreover, CocoSciSum is a user-centric toolkit, supporting user preferences expressed in natural language instructions, and accommodating diverse input document formats. CocoSciSum is available on GitHub (https://github.com/WING-NUS/SciAssist/tree/CocoSciSum) with an introduction video (https://youtu.be/YC1YDeEjAbQ).", } -@inproceedings{jain-etal-2022-comparative, - title = "Comparative Snippet Generation", - author = "Jain, Saurabh and - Miao, Yisong and +@inproceedings{benotti-etal-2023-understanding, + title = "Understanding Ethics in {NLP} Authoring and Reviewing", + author = {Benotti, Luciana and + Fort, Kar{\"e}n and + Kan, Min-Yen and + Tsvetkov, Yulia}, + editor = "Zanzotto, Fabio Massimo and + Pradhan, Sameer", + booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts", + month = may, + year = "2023", + address = "Dubrovnik, Croatia", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.eacl-tutorials.4", + doi = "10.18653/v1/2023.eacl-tutorials.4", + pages = "19--24", + abstract = "With NLP research now quickly being transferred into real-world applications, it is important to be aware of and think through the consequences of our scientific investigation. Such ethical considerations are important in both authoring and reviewing. This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues and review common considerations that recur in NLP research. The methodology is interactive and participatory, including case studies and working in groups. Importantly, the participants will be co-building the tutorial outcomes and will be working to create further tutorial materials to share as public outcomes.", +} + +@inproceedings{malik-etal-2023-udapter, + title = "{UDAPTER} - Efficient Domain Adaptation Using Adapters", + author = "Malik, Bhavitvya and + Ramesh Kashyap, Abhinav and + Kan, Min-Yen and + Poria, Soujanya", + editor = "Vlachos, Andreas and + Augenstein, Isabelle", + booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", + month = may, + year = "2023", + address = "Dubrovnik, Croatia", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.eacl-main.165", + doi = "10.18653/v1/2023.eacl-main.165", + pages = "2249--2263", + abstract = "We propose two methods to make unsupervised domain adaptation (UDA) more parameter efficient using adapters {--} small bottleneck layers interspersed with every layer of the large-scale pre-trained language model (PLM). The first method deconstructs UDA into a two-step process: first by adding a domain adapter to learn domain-invariant information and then by adding a task adapter that uses domain-invariant information to learn task representations in the source domain. The second method jointly learns a supervised classifier while reducing the divergence measure. Compared to strong baselines, our simple methods perform well in natural language inference (MNLI) and the cross-domain sentiment classification task. We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0.85{\%} F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters. We release our code at this URL.", +} + + +@inproceedings{chow-etal-2023-travlr, + title = "{T}ra{VLR}: Now You See It, Now You Don{'}t! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoning", + author = "Chow, Keng Ji and + Tan, Samson and Kan, Min-Yen", - editor = "Malmasi, Shervin and - Rokhlenko, Oleg and - Ueffing, Nicola and - Guy, Ido and - Agichtein, Eugene and - Kallumadi, Surya", - booktitle = "Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)", + editor = "Vlachos, Andreas and + Augenstein, Isabelle", + booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", + month = may, + year = "2023", + address = "Dubrovnik, Croatia", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.eacl-main.242", + doi = "10.18653/v1/2023.eacl-main.242", + pages = "3322--3347", + abstract = "Numerous visio-linguistic (V+L) representation learning methods have been developed, yet existing datasets do not adequately evaluate the extent to which they represent visual and linguistic concepts in a unified space. We propose several novel evaluation settings for V+L models, including cross-modal transfer. Furthermore, existing V+L benchmarks often report global accuracy scores on the entire dataset, making it difficult to pinpoint the specific reasoning tasks that models fail and succeed at. We present TraVLR, a synthetic dataset comprising four V+L reasoning tasks. TraVLR{'}s synthetic nature allows us to constrain its training and testing distributions along task-relevant dimensions, enabling the evaluation of out-of-distribution generalisation. Each example in TraVLR redundantly encodes the scene in two modalities, allowing either to be dropped or added during training or testing without losing relevant information. We compare the performance of four state-of-the-art V+L models, finding that while they perform well on test examples from the same modality, they all fail at cross-modal transfer and have limited success accommodating the addition or deletion of one modality. We release TraVLR as an open challenge for the research community.", +} + +@inproceedings{ou-etal-2023-songs, + title = "Songs Across Borders: Singable and Controllable Neural Lyric Translation", + author = "Ou, Longshen and + Ma, Xichu and + Kan, Min-Yen and + Wang, Ye", + editor = "Rogers, Anna and + Boyd-Graber, Jordan and + Okazaki, Naoaki", + booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jul, + year = "2023", + address = "Toronto, Canada", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.acl-long.27", + doi = "10.18653/v1/2023.acl-long.27", + pages = "447--467", + abstract = "The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to prompt-driven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system. Our model achieves 99.85{\%}, 99.00{\%}, and 95.52{\%} on length accuracy, rhyme accuracy, and word boundary recall. In our subjective evaluation, our model shows a 75{\%} relative enhancement on overall quality, compared against naive fine-tuning (Code available at \url{https://github.com/Sonata165/ControllableLyricTranslation}).", +} + +@inproceedings{aksu-etal-2023-prompter, + title = "Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation", + author = "Aksu, Ibrahim Taha and + Kan, Min-Yen and + Chen, Nancy", + editor = "Rogers, Anna and + Boyd-Graber, Jordan and + Okazaki, Naoaki", + booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jul, + year = "2023", + address = "Toronto, Canada", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.acl-long.252", + doi = "10.18653/v1/2023.acl-long.252", + pages = "4588--4603", + abstract = "A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data {---} zero-shot domain adaptation. Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem due to its robustness. However, it has yet to be applied to the zero-shot scenarios, as it is not clear how to apply it unsupervisedly. Our method, Prompter, uses descriptions of target domain slots to generate dynamic prefixes that are concatenated to the key and values at each layer{'}s self-attention mechanism. This allows for the use of prefix-tuning in zero-shot. Prompter outperforms previous methods on both the MultiWOZ and SGD benchmarks. In generating prefixes, our analyses find that Prompter not only utilizes the semantics of slot descriptions but also how often the slots appear together in conversation. Moreover, Prompter{'}s gains are due to its improved ability to distinguish {''}none{''}-valued dialogue slots, compared against baselines.", +} + +@inproceedings{pan-etal-2023-fact, + title = "Fact-Checking Complex Claims with Program-Guided Reasoning", + author = "Pan, Liangming and + Wu, Xiaobao and + Lu, Xinyuan and + Luu, Anh Tuan and + Wang, William Yang and + Kan, Min-Yen and + Nakov, Preslav", + editor = "Rogers, Anna and + Boyd-Graber, Jordan and + Okazaki, Naoaki", + booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jul, + year = "2023", + address = "Toronto, Canada", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.acl-long.386", + doi = "10.18653/v1/2023.acl-long.386", + pages = "6981--7004", + abstract = "Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning. In this paper, we present Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions. We first leverage the in-context learning ability of large language models to generate reasoning programs to guide the verification process. Afterward, we execute the program by delegating each sub-task to the corresponding sub-task handler. This process makes our model both explanatory and data-efficient, providing clear explanations of its reasoning process and requiring minimal training data. We evaluate ProgramFC on two challenging fact-checking datasets and show that it outperforms seven fact-checking baselines across different settings of evidence availability, with explicit output programs that benefit human debugging. Our codes and data are publicly available at \url{https://github.com/mbzuai-nlp/ProgramFC}.", +} + +@inproceedings{zhang-etal-2022-interpreting, + title = "Interpreting the Robustness of Neural {NLP} Models to Textual Perturbations", + author = "Zhang, Yunxiang and + Pan, Liangming and + Tan, Samson and + Kan, Min-Yen", + editor = "Muresan, Smaranda and + Nakov, Preslav and + Villavicencio, Aline", + booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.ecnlp-1.7", - doi = "10.18653/v1/2022.ecnlp-1.7", - pages = "49--57", - abstract = "We model products{'} reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generation from contrasting opinions regarding a product, and an analysis of performance of a pre-trained BERT model to generate such snippets.", + url = "https://aclanthology.org/2022.findings-acl.315", + doi = "10.18653/v1/2022.findings-acl.315", + pages = "3993--4007", + abstract = "Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear why models are less robust to some perturbations than others. In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence). We further give a causal justification for the learnability metric. We conduct extensive experiments with four prominent NLP models {---} TextRNN, BERT, RoBERTa and XLNet {---} over eight types of textual perturbations on three datasets. We show that a model which is better at identifying a perturbation (higher learnability) becomes worse at ignoring such a perturbation at test time (lower robustness), providing empirical support for our hypothesis.", } @inproceedings{xu-etal-2022-corefdiffs, @@ -160,75 +466,3 @@ @inproceedings{xu-etal-2022-corefdiffs pages = "471--484", abstract = "Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5{\%}, 7.4{\%} and 8.2{\%} on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation.", } - -@inproceedings{ramesh-kashyap-etal-2022-different, - title = "So Different Yet So Alike! Constrained Unsupervised Text Style Transfer", - author = "Ramesh Kashyap, Abhinav and - Hazarika, Devamanyu and - Kan, Min-Yen and - Zimmermann, Roger and - Poria, Soujanya", - editor = "Muresan, Smaranda and - Nakov, Preslav and - Villavicencio, Aline", - booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = may, - year = "2022", - address = "Dublin, Ireland", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.acl-long.32", - doi = "10.18653/v1/2022.acl-long.32", - pages = "416--431", - abstract = "Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content while adapting to the target domain. However, it does not explicitly maintain other attributes between the source and translated text: e.g., text length and descriptiveness. Maintaining constraints in transfer has several downstream applications, including data augmentation and debiasing. We introduce a method for such constrained unsupervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooperate and reduce the same loss. The first is a contrastive loss and the second is a classification loss {---} aiming to regularize the latent space further and bring similar sentences closer together. We demonstrate that such training retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures.", -} - -@inproceedings{qin-etal-2022-gl, - title = "{GL}-{CL}e{F}: A Global{--}Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding", - author = "Qin, Libo and - Chen, Qiguang and - Xie, Tianbao and - Li, Qixin and - Lou, Jian-Guang and - Che, Wanxiang and - Kan, Min-Yen", - editor = "Muresan, Smaranda and - Nakov, Preslav and - Villavicencio, Aline", - booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = may, - year = "2022", - address = "Dublin, Ireland", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.acl-long.191", - doi = "10.18653/v1/2022.acl-long.191", - pages = "2677--2686", - abstract = "Due to high data demands of current methods, attention to zero-shot cross-lingual spoken language understanding (SLU) has grown, as such approaches greatly reduce human annotation effort. However, existing models solely rely on shared parameters, which can only perform implicit alignment across languages. We present Global-Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming. Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages. In addition, a key step in GL-CLeF is a proposed Local and Global component, which achieves a fine-grained cross-lingual transfer (i.e., sentence-level Local intent transfer, token-level Local slot transfer, and semantic-level Global transfer across intent and slot). Experiments on MultiATIS++ show that GL-CLeF achieves the best performance and successfully pulls representations of similar sentences across languages closer.", -} - -@inproceedings{aksu-etal-2021-velocidapter, - title = "Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation", - author = "Aksu, Ibrahim Taha and - Liu, Zhengyuan and - Kan, Min-Yen and - Chen, Nancy", - editor = "Li, Haizhou and - Levow, Gina-Anne and - Yu, Zhou and - Gupta, Chitralekha and - Sisman, Berrak and - Cai, Siqi and - Vandyke, David and - Dethlefs, Nina and - Wu, Yan and - Li, Junyi Jessy", - booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue", - month = jul, - year = "2021", - address = "Singapore and Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2021.sigdial-1.14", - doi = "10.18653/v1/2021.sigdial-1.14", - pages = "133--143", - abstract = "We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension. Velocidapter augments datasets by simulating synthetic conversations for a task-oriented dialogue domain, requiring a small amount of bootstrapping work for each new domain. We evaluate the efficacy of our framework on a task-oriented dialogue comprehension dataset, MRCWOZ, which we curate by annotating questions for slots in the restaurant, taxi, and hotel domains of the MultiWOZ 2.2 dataset (Zang et al., 2020). We run experiments within a low-resource setting, where we pretrain a model on SQuAD, fine-tuning it on either a small original data or on the synthetic data generated by our framework. Velocidapter shows significant improvements using both the transformer-based BERTBase and BiDAF as base models. We further show that the framework is easy to use by novice users and conclude that Velocidapter can greatly help training over task-oriented dialogues, especially for low-resourced emerging domains.", -} - From 2043480463799a75656707161f8485ad1bbb41cc Mon Sep 17 00:00:00 2001 From: knmnyn <217067+knmnyn@users.noreply.github.com> Date: Sat, 6 Jul 2024 02:22:25 +0000 Subject: [PATCH 08/22] content: import publications from Bibtex --- .../aksu-etal-2023-prompter/cite.bib | 18 +++++++++ .../aksu-etal-2023-prompter/index.md | 29 ++++++++++++++ .../benotti-etal-2023-understanding/cite.bib | 18 +++++++++ .../benotti-etal-2023-understanding/index.md | 26 +++++++++++++ .../chow-etal-2023-travlr/cite.bib | 17 +++++++++ .../chow-etal-2023-travlr/index.md | 34 +++++++++++++++++ .../diao-etal-2023-doolittle/cite.bib | 23 +++++++++++ .../diao-etal-2023-doolittle/index.md | 38 +++++++++++++++++++ .../ding-etal-2023-cocoscisum/cite.bib | 18 +++++++++ .../ding-etal-2023-cocoscisum/index.md | 30 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Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem due to its robustness. However, it has yet to be applied to the zero-shot scenarios, as it is not clear how to apply it unsupervisedly. Our method, Prompter, uses descriptions of target domain slots to generate dynamic prefixes that are concatenated to the key and values at each layer′s self-attention mechanism. This allows for the use of prefix-tuning in zero-shot. Prompter outperforms previous methods on both the MultiWOZ and SGD benchmarks. In generating prefixes, our analyses find that Prompter not only utilizes the semantics of slot descriptions but also how often the slots appear together in conversation. Moreover, Prompter′s gains are due to its improved ability to distinguish ″none″-valued dialogue slots, compared against baselines.}, + address = {Toronto, Canada}, + author = {Aksu, Ibrahim Taha and +Kan, Min-Yen and +Chen, Nancy}, + booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/2023.acl-long.252}, + editor = {Rogers, Anna and +Boyd-Graber, Jordan and +Okazaki, Naoaki}, + month = {July}, + pages = {4588--4603}, + publisher = {Association for Computational Linguistics}, + title = {Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation}, + url = {https://aclanthology.org/2023.acl-long.252}, + year = {2023} +} diff --git a/content/publication/aksu-etal-2023-prompter/index.md b/content/publication/aksu-etal-2023-prompter/index.md new file mode 100644 index 0000000..c26f736 --- /dev/null +++ b/content/publication/aksu-etal-2023-prompter/index.md @@ -0,0 +1,29 @@ +--- +title: 'Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation' +authors: +- Ibrahim Taha Aksu +- Min-Yen Kan +- Nancy Chen +date: '2023-07-01' +publishDate: '2024-07-06T02:22:24.632344Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 61st Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/2023.acl-long.252 +abstract: A challenge in the Dialogue State Tracking (DST) field is adapting models + to new domains without using any supervised data --- zero-shot domain adaptation. + Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem + due to its robustness. However, it has yet to be applied to the zero-shot scenarios, + as it is not clear how to apply it unsupervisedly. Our method, Prompter, uses descriptions + of target domain slots to generate dynamic prefixes that are concatenated to the + key and values at each layer′s self-attention mechanism. This allows for the use + of prefix-tuning in zero-shot. Prompter outperforms previous methods on both the + MultiWOZ and SGD benchmarks. In generating prefixes, our analyses find that Prompter + not only utilizes the semantics of slot descriptions but also how often the slots + appear together in conversation. Moreover, Prompter′s gains are due to its improved + ability to distinguish ″none″-valued dialogue slots, compared against baselines. +links: +- name: URL + url: https://aclanthology.org/2023.acl-long.252 +--- diff --git a/content/publication/benotti-etal-2023-understanding/cite.bib b/content/publication/benotti-etal-2023-understanding/cite.bib new file mode 100644 index 0000000..1770d61 --- /dev/null +++ b/content/publication/benotti-etal-2023-understanding/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{benotti-etal-2023-understanding, + abstract = {With NLP research now quickly being transferred into real-world applications, it is important to be aware of and think through the consequences of our scientific investigation. Such ethical considerations are important in both authoring and reviewing. This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues and review common considerations that recur in NLP research. The methodology is interactive and participatory, including case studies and working in groups. Importantly, the participants will be co-building the tutorial outcomes and will be working to create further tutorial materials to share as public outcomes.}, + address = {Dubrovnik, Croatia}, + author = {Benotti, Luciana and +Fort, Karën and +Kan, Min-Yen and +Tsvetkov, Yulia}, + booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts}, + doi = {10.18653/v1/2023.eacl-tutorials.4}, + editor = {Zanzotto, Fabio Massimo and +Pradhan, Sameer}, + month = {May}, + pages = {19--24}, + publisher = {Association for Computational Linguistics}, + title = {Understanding Ethics in NLP Authoring and Reviewing}, + url = {https://aclanthology.org/2023.eacl-tutorials.4}, + year = {2023} +} diff --git a/content/publication/benotti-etal-2023-understanding/index.md b/content/publication/benotti-etal-2023-understanding/index.md new file mode 100644 index 0000000..d4dc97b --- /dev/null +++ b/content/publication/benotti-etal-2023-understanding/index.md @@ -0,0 +1,26 @@ +--- +title: Understanding Ethics in NLP Authoring and Reviewing +authors: +- Luciana Benotti +- Karën Fort +- Min-Yen Kan +- Yulia Tsvetkov +date: '2023-05-01' +publishDate: '2024-07-06T02:22:24.603387Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 17th Conference of the European Chapter of the Association + for Computational Linguistics: Tutorial Abstracts*' +doi: 10.18653/v1/2023.eacl-tutorials.4 +abstract: With NLP research now quickly being transferred into real-world applications, + it is important to be aware of and think through the consequences of our scientific + investigation. Such ethical considerations are important in both authoring and reviewing. + This tutorial will equip participants with basic guidelines for thinking deeply + about ethical issues and review common considerations that recur in NLP research. + The methodology is interactive and participatory, including case studies and working + in groups. Importantly, the participants will be co-building the tutorial outcomes + and will be working to create further tutorial materials to share as public outcomes. +links: +- name: URL + url: https://aclanthology.org/2023.eacl-tutorials.4 +--- diff --git a/content/publication/chow-etal-2023-travlr/cite.bib b/content/publication/chow-etal-2023-travlr/cite.bib new file mode 100644 index 0000000..6121057 --- /dev/null +++ b/content/publication/chow-etal-2023-travlr/cite.bib @@ -0,0 +1,17 @@ +@inproceedings{chow-etal-2023-travlr, + abstract = {Numerous visio-linguistic (V+L) representation learning methods have been developed, yet existing datasets do not adequately evaluate the extent to which they represent visual and linguistic concepts in a unified space. We propose several novel evaluation settings for V+L models, including cross-modal transfer. Furthermore, existing V+L benchmarks often report global accuracy scores on the entire dataset, making it difficult to pinpoint the specific reasoning tasks that models fail and succeed at. We present TraVLR, a synthetic dataset comprising four V+L reasoning tasks. TraVLR′s synthetic nature allows us to constrain its training and testing distributions along task-relevant dimensions, enabling the evaluation of out-of-distribution generalisation. Each example in TraVLR redundantly encodes the scene in two modalities, allowing either to be dropped or added during training or testing without losing relevant information. We compare the performance of four state-of-the-art V+L models, finding that while they perform well on test examples from the same modality, they all fail at cross-modal transfer and have limited success accommodating the addition or deletion of one modality. We release TraVLR as an open challenge for the research community.}, + address = {Dubrovnik, Croatia}, + author = {Chow, Keng Ji and +Tan, Samson and +Kan, Min-Yen}, + booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics}, + doi = {10.18653/v1/2023.eacl-main.242}, + editor = {Vlachos, Andreas and +Augenstein, Isabelle}, + month = {May}, + pages = {3322--3347}, + publisher = {Association for Computational Linguistics}, + title = {TraVLR: Now You See It, Now You Don′t! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoning}, + url = {https://aclanthology.org/2023.eacl-main.242}, + year = {2023} +} diff --git a/content/publication/chow-etal-2023-travlr/index.md b/content/publication/chow-etal-2023-travlr/index.md new file mode 100644 index 0000000..fb2681d --- /dev/null +++ b/content/publication/chow-etal-2023-travlr/index.md @@ -0,0 +1,34 @@ +--- +title: 'TraVLR: Now You See It, Now You Don′t! A Bimodal Dataset for Evaluating Visio-Linguistic + Reasoning' +authors: +- Keng Ji Chow +- Samson Tan +- Min-Yen Kan +date: '2023-05-01' +publishDate: '2024-07-06T02:22:24.618335Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 17th Conference of the European Chapter of the Association + for Computational Linguistics*' +doi: 10.18653/v1/2023.eacl-main.242 +abstract: Numerous visio-linguistic (V+L) representation learning methods have been + developed, yet existing datasets do not adequately evaluate the extent to which + they represent visual and linguistic concepts in a unified space. We propose several + novel evaluation settings for V+L models, including cross-modal transfer. Furthermore, + existing V+L benchmarks often report global accuracy scores on the entire dataset, + making it difficult to pinpoint the specific reasoning tasks that models fail and + succeed at. We present TraVLR, a synthetic dataset comprising four V+L reasoning + tasks. TraVLR′s synthetic nature allows us to constrain its training and testing + distributions along task-relevant dimensions, enabling the evaluation of out-of-distribution + generalisation. Each example in TraVLR redundantly encodes the scene in two modalities, + allowing either to be dropped or added during training or testing without losing + relevant information. We compare the performance of four state-of-the-art V+L models, + finding that while they perform well on test examples from the same modality, they + all fail at cross-modal transfer and have limited success accommodating the addition + or deletion of one modality. We release TraVLR as an open challenge for the research + community. +links: +- name: URL + url: https://aclanthology.org/2023.eacl-main.242 +--- diff --git a/content/publication/diao-etal-2023-doolittle/cite.bib b/content/publication/diao-etal-2023-doolittle/cite.bib new file mode 100644 index 0000000..6b7a6b4 --- /dev/null +++ b/content/publication/diao-etal-2023-doolittle/cite.bib @@ -0,0 +1,23 @@ +@inproceedings{diao-etal-2023-doolittle, + abstract = {Improving the quality of academic writing is a meaningful but challenging task. Conventional methods of language refinement focus on narrow, specific linguistic features within isolated sentences, such as grammatical errors and improper word use. We propose a more general task, Academic Writing Formalization (AWF), to improve the overall quality of formal academic writing at the paragraph level. We formulate this language refinement task as a formal text style transfer task which transfers informal-academic text to formal-academic and contribute a large-scale non-parallel dataset, Doolittle, for this purpose. Concurrently, we apply a method named metric-oriented reinforcement learning (MORL) to two large language models (LLM) where we incorporate different levels of automatic feedback into the training process. Our experiments reveal that existing text transfer models and grammatical error correction models address certain aspects of AWF but still have a significant performance gap compared to human performance. Meanwhile, language models fine-tuned with our MORL method exhibit considerably improved performance, rivaling the latest chatbot ChatGPT, but still have a non-negligible gap compared to the ground truth formal-academic texts in Doolittle.}, + address = {Singapore}, + author = {Diao, Shizhe and +Lei, Yongyu and +Pan, Liangming and +Fang, Tianqing and +Zhou, Wangchunshu and +Keh, Sedrick and +Kan, Min-Yen and +Zhang, Tong}, + booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing}, + doi = {10.18653/v1/2023.emnlp-main.809}, + editor = {Bouamor, Houda and +Pino, Juan and +Bali, Kalika}, + month = {December}, + pages = {13093--13111}, + publisher = {Association for Computational Linguistics}, + title = {Doolittle: Benchmarks and Corpora for Academic Writing Formalization}, + url = {https://aclanthology.org/2023.emnlp-main.809}, + year = {2023} +} diff --git a/content/publication/diao-etal-2023-doolittle/index.md b/content/publication/diao-etal-2023-doolittle/index.md new file mode 100644 index 0000000..9f6751a --- /dev/null +++ b/content/publication/diao-etal-2023-doolittle/index.md @@ -0,0 +1,38 @@ +--- +title: 'Doolittle: Benchmarks and Corpora for Academic Writing Formalization' +authors: +- Shizhe Diao +- Yongyu Lei +- Liangming Pan +- Tianqing Fang +- Wangchunshu Zhou +- Sedrick Keh +- Min-Yen Kan +- Tong Zhang +date: '2023-12-01' +publishDate: '2024-07-06T02:22:24.582277Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2023 Conference on Empirical Methods in Natural + Language Processing*' +doi: 10.18653/v1/2023.emnlp-main.809 +abstract: Improving the quality of academic writing is a meaningful but challenging + task. Conventional methods of language refinement focus on narrow, specific linguistic + features within isolated sentences, such as grammatical errors and improper word + use. We propose a more general task, Academic Writing Formalization (AWF), to improve + the overall quality of formal academic writing at the paragraph level. We formulate + this language refinement task as a formal text style transfer task which transfers + informal-academic text to formal-academic and contribute a large-scale non-parallel + dataset, Doolittle, for this purpose. Concurrently, we apply a method named metric-oriented + reinforcement learning (MORL) to two large language models (LLM) where we incorporate + different levels of automatic feedback into the training process. Our experiments + reveal that existing text transfer models and grammatical error correction models + address certain aspects of AWF but still have a significant performance gap compared + to human performance. Meanwhile, language models fine-tuned with our MORL method + exhibit considerably improved performance, rivaling the latest chatbot ChatGPT, + but still have a non-negligible gap compared to the ground truth formal-academic + texts in Doolittle. +links: +- name: URL + url: https://aclanthology.org/2023.emnlp-main.809 +--- diff --git a/content/publication/ding-etal-2023-cocoscisum/cite.bib b/content/publication/ding-etal-2023-cocoscisum/cite.bib new file mode 100644 index 0000000..180f8f3 --- /dev/null +++ b/content/publication/ding-etal-2023-cocoscisum/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{ding-etal-2023-cocoscisum, + abstract = {We present a novel toolkit for controlled summarization of scientific documents, designed for the specific needs of the scientific community. Our system generates summaries based on user preferences, adjusting key attributes specifically of length and keyword inclusion. A distinguishing feature is its ability to manage multiple attributes concurrently, demonstrating Compositional Controllability for Scientific Summarization (CocoSciSum). Benchmarked against the strong Flan-T5 baseline, CocoSciSum exhibits superior performance on both the quality of summaries generated and the control over single and multiple attributes. Moreover, CocoSciSum is a user-centric toolkit, supporting user preferences expressed in natural language instructions, and accommodating diverse input document formats. CocoSciSum is available on GitHub (https://github.com/WING-NUS/SciAssist/tree/CocoSciSum) with an introduction video (https://youtu.be/YC1YDeEjAbQ).}, + address = {Singapore}, + author = {Ding, Yixi and +Qin, Yanxia and +Liu, Qian and +Kan, Min-Yen}, + booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, + doi = {10.18653/v1/2023.emnlp-demo.47}, + editor = {Feng, Yansong and +Lefever, Els}, + month = {December}, + pages = {518--526}, + publisher = {Association for Computational Linguistics}, + title = {CocoSciSum: A Scientific Summarization Toolkit with Compositional Controllability}, + url = {https://aclanthology.org/2023.emnlp-demo.47}, + year = {2023} +} diff --git a/content/publication/ding-etal-2023-cocoscisum/index.md b/content/publication/ding-etal-2023-cocoscisum/index.md new file mode 100644 index 0000000..97b3503 --- /dev/null +++ b/content/publication/ding-etal-2023-cocoscisum/index.md @@ -0,0 +1,30 @@ +--- +title: 'CocoSciSum: A Scientific Summarization Toolkit with Compositional Controllability' +authors: +- Yixi Ding +- Yanxia Qin +- Qian Liu +- Min-Yen Kan +date: '2023-12-01' +publishDate: '2024-07-06T02:22:24.596464Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2023 Conference on Empirical Methods in Natural + Language Processing: System Demonstrations*' +doi: 10.18653/v1/2023.emnlp-demo.47 +abstract: We present a novel toolkit for controlled summarization of scientific documents, + designed for the specific needs of the scientific community. Our system generates + summaries based on user preferences, adjusting key attributes specifically of length + and keyword inclusion. A distinguishing feature is its ability to manage multiple + attributes concurrently, demonstrating Compositional Controllability for Scientific + Summarization (CocoSciSum). Benchmarked against the strong Flan-T5 baseline, CocoSciSum + exhibits superior performance on both the quality of summaries generated and the + control over single and multiple attributes. Moreover, CocoSciSum is a user-centric + toolkit, supporting user preferences expressed in natural language instructions, + and accommodating diverse input document formats. CocoSciSum is available on GitHub + (https://github.com/WING-NUS/SciAssist/tree/CocoSciSum) with an introduction video + (https://youtu.be/YC1YDeEjAbQ). +links: +- name: URL + url: https://aclanthology.org/2023.emnlp-demo.47 +--- diff --git a/content/publication/li-etal-2023-coannotating/cite.bib b/content/publication/li-etal-2023-coannotating/cite.bib new file mode 100644 index 0000000..b6324da --- /dev/null +++ b/content/publication/li-etal-2023-coannotating/cite.bib @@ -0,0 +1,22 @@ +@inproceedings{li-etal-2023-coannotating, + abstract = {Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many text-annotation tasks, comparable with or even exceeding human annotators. Such LLMs can serve as alternatives for manual annotation, due to lower costs and higher scalability. However, limited work has leveraged LLMs as complementary annotators, nor explored how annotation work is best allocated among humans and LLMs to achieve both quality and cost objectives. We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Under this framework, we utilize uncertainty to estimate LLMs′ annotation capability. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline. For code implementation, see https://github.com/SALT-NLP/CoAnnotating.}, + address = {Singapore}, + author = {Li, Minzhi and +Shi, Taiwei and +Ziems, Caleb and +Kan, Min-Yen and +Chen, Nancy and +Liu, Zhengyuan and +Yang, Diyi}, + booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing}, + doi = {10.18653/v1/2023.emnlp-main.92}, + editor = {Bouamor, Houda and +Pino, Juan and +Bali, Kalika}, + month = {December}, + pages = {1487--1505}, + publisher = {Association for Computational Linguistics}, + title = {CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation}, + url = {https://aclanthology.org/2023.emnlp-main.92}, + year = {2023} +} diff --git a/content/publication/li-etal-2023-coannotating/index.md b/content/publication/li-etal-2023-coannotating/index.md new file mode 100644 index 0000000..d101c50 --- /dev/null +++ b/content/publication/li-etal-2023-coannotating/index.md @@ -0,0 +1,35 @@ +--- +title: 'CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language + Models for Data Annotation' +authors: +- Minzhi Li +- Taiwei Shi +- Caleb Ziems +- Min-Yen Kan +- Nancy Chen +- Zhengyuan Liu +- Diyi Yang +date: '2023-12-01' +publishDate: '2024-07-06T02:22:24.561215Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2023 Conference on Empirical Methods in Natural + Language Processing*' +doi: 10.18653/v1/2023.emnlp-main.92 +abstract: Annotated data plays a critical role in Natural Language Processing (NLP) + in training models and evaluating their performance. Given recent developments in + Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability + on many text-annotation tasks, comparable with or even exceeding human annotators. + Such LLMs can serve as alternatives for manual annotation, due to lower costs and + higher scalability. However, limited work has leveraged LLMs as complementary annotators, + nor explored how annotation work is best allocated among humans and LLMs to achieve + both quality and cost objectives. We propose CoAnnotating, a novel paradigm for + Human-LLM co-annotation of unstructured texts at scale. Under this framework, we + utilize uncertainty to estimate LLMs′ annotation capability. Our empirical study + shows CoAnnotating to be an effective means to allocate work from results on different + datasets, with up to 21% performance improvement over random baseline. For code + implementation, see https://github.com/SALT-NLP/CoAnnotating. +links: +- name: URL + url: https://aclanthology.org/2023.emnlp-main.92 +--- diff --git a/content/publication/li-etal-2024-uno/cite.bib b/content/publication/li-etal-2024-uno/cite.bib new file mode 100644 index 0000000..c58e5fe --- /dev/null +++ b/content/publication/li-etal-2024-uno/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{li-etal-2024-uno, + abstract = {Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, but ignore unlabelled data in the target domain.We transform zero-shot DST into few-shot DST by utilising such unlabelled data via joint and self-training methods. Our method incorporates auxiliary tasks that generate slot types as inverse prompts for main tasks, creating slot values during joint training. Cycle consistency between these two tasks enables the generation and selection of quality samples in unknown target domains for subsequent fine-tuning. This approach also facilitates automatic label creation, thereby optimizing the training and fine-tuning of DST models. We demonstrate this method′s effectiveness on general language models in zero-shot scenarios, improving average joint goal accuracy by 8% across all domains in MultiWOZ.}, + address = {Mexico City, Mexico}, + author = {Li, Chuang and +Zhang, Yan and +Kan, Min-Yen and +Li, Haizhou}, + booktitle = {Findings of the Association for Computational Linguistics: NAACL 2024}, + editor = {Duh, Kevin and +Gomez, Helena and +Bethard, Steven}, + month = {June}, + pages = {2972--2983}, + publisher = {Association for Computational Linguistics}, + title = {UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking}, + url = {https://aclanthology.org/2024.findings-naacl.187}, + year = {2024} +} diff --git a/content/publication/li-etal-2024-uno/index.md b/content/publication/li-etal-2024-uno/index.md new file mode 100644 index 0000000..945d0a0 --- /dev/null +++ b/content/publication/li-etal-2024-uno/index.md @@ -0,0 +1,27 @@ +--- +title: 'UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking' +authors: +- Chuang Li +- Yan Zhang +- Min-Yen Kan +- Haizhou Li +date: '2024-06-01' +publishDate: '2024-07-06T02:22:24.510368Z' +publication_types: +- paper-conference +publication: '*Findings of the Association for Computational Linguistics: NAACL 2024*' +abstract: Previous zero-shot dialogue state tracking (DST) methods only apply transfer + learning, but ignore unlabelled data in the target domain.We transform zero-shot + DST into few-shot DST by utilising such unlabelled data via joint and self-training + methods. Our method incorporates auxiliary tasks that generate slot types as inverse + prompts for main tasks, creating slot values during joint training. Cycle consistency + between these two tasks enables the generation and selection of quality samples + in unknown target domains for subsequent fine-tuning. This approach also facilitates + automatic label creation, thereby optimizing the training and fine-tuning of DST + models. We demonstrate this method′s effectiveness on general language models in + zero-shot scenarios, improving average joint goal accuracy by 8% across all domains + in MultiWOZ. +links: +- name: URL + url: https://aclanthology.org/2024.findings-naacl.187 +--- diff --git a/content/publication/lu-etal-2023-scitab/cite.bib b/content/publication/lu-etal-2023-scitab/cite.bib new file mode 100644 index 0000000..1811361 --- /dev/null +++ b/content/publication/lu-etal-2023-scitab/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{lu-etal-2023-scitab, + abstract = {Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence. We present SCITAB, a challenging evaluation dataset consisting of 1.2K expert-verified scientific claims that 1) originate from authentic scientific publications and 2) require compositional reasoning for verification. The claims are paired with evidence-containing scientific tables annotated with labels. Through extensive evaluations, we demonstrate that SCITAB poses a significant challenge to state-of-the-art models, including table-based pretraining models and large language models. All models except GPT-4 achieved performance barely above random guessing. Popular prompting techniques, such as Chain-of-Thought, do not achieve much performance gains on SCITAB. Our analysis uncovers several unique challenges posed by SCITAB, including table grounding, claim ambiguity, and compositional reasoning. Our codes and data are publicly available at https://github.com/XinyuanLu00/SciTab.}, + address = {Singapore}, + author = {Lu, Xinyuan and +Pan, Liangming and +Liu, Qian and +Nakov, Preslav and +Kan, Min-Yen}, + booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing}, + doi = {10.18653/v1/2023.emnlp-main.483}, + editor = {Bouamor, Houda and +Pino, Juan and +Bali, Kalika}, + month = {December}, + pages = {7787--7813}, + publisher = {Association for Computational Linguistics}, + title = {SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables}, + url = {https://aclanthology.org/2023.emnlp-main.483}, + year = {2023} +} diff --git a/content/publication/lu-etal-2023-scitab/index.md b/content/publication/lu-etal-2023-scitab/index.md new file mode 100644 index 0000000..918745c --- /dev/null +++ b/content/publication/lu-etal-2023-scitab/index.md @@ -0,0 +1,33 @@ +--- +title: 'SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification + on Scientific Tables' +authors: +- Xinyuan Lu +- Liangming Pan +- Qian Liu +- Preslav Nakov +- Min-Yen Kan +date: '2023-12-01' +publishDate: '2024-07-06T02:22:24.568376Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2023 Conference on Empirical Methods in Natural + Language Processing*' +doi: 10.18653/v1/2023.emnlp-main.483 +abstract: Current scientific fact-checking benchmarks exhibit several shortcomings, + such as biases arising from crowd-sourced claims and an over-reliance on text-based + evidence. We present SCITAB, a challenging evaluation dataset consisting of 1.2K + expert-verified scientific claims that 1) originate from authentic scientific publications + and 2) require compositional reasoning for verification. The claims are paired with + evidence-containing scientific tables annotated with labels. Through extensive evaluations, + we demonstrate that SCITAB poses a significant challenge to state-of-the-art models, + including table-based pretraining models and large language models. All models except + GPT-4 achieved performance barely above random guessing. Popular prompting techniques, + such as Chain-of-Thought, do not achieve much performance gains on SCITAB. Our analysis + uncovers several unique challenges posed by SCITAB, including table grounding, claim + ambiguity, and compositional reasoning. Our codes and data are publicly available + at https://github.com/XinyuanLu00/SciTab. +links: +- name: URL + url: https://aclanthology.org/2023.emnlp-main.483 +--- diff --git a/content/publication/malik-etal-2023-udapter/cite.bib b/content/publication/malik-etal-2023-udapter/cite.bib new file mode 100644 index 0000000..b83e52f --- /dev/null +++ b/content/publication/malik-etal-2023-udapter/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{malik-etal-2023-udapter, + abstract = {We propose two methods to make unsupervised domain adaptation (UDA) more parameter efficient using adapters -- small bottleneck layers interspersed with every layer of the large-scale pre-trained language model (PLM). The first method deconstructs UDA into a two-step process: first by adding a domain adapter to learn domain-invariant information and then by adding a task adapter that uses domain-invariant information to learn task representations in the source domain. The second method jointly learns a supervised classifier while reducing the divergence measure. Compared to strong baselines, our simple methods perform well in natural language inference (MNLI) and the cross-domain sentiment classification task. We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0.85% F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters. We release our code at this URL.}, + address = {Dubrovnik, Croatia}, + author = {Malik, Bhavitvya and +Ramesh Kashyap, Abhinav and +Kan, Min-Yen and +Poria, Soujanya}, + booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics}, + doi = {10.18653/v1/2023.eacl-main.165}, + editor = {Vlachos, Andreas and +Augenstein, Isabelle}, + month = {May}, + pages = {2249--2263}, + publisher = {Association for Computational Linguistics}, + title = {UDAPTER - Efficient Domain Adaptation Using Adapters}, + url = {https://aclanthology.org/2023.eacl-main.165}, + year = {2023} +} diff --git a/content/publication/malik-etal-2023-udapter/index.md b/content/publication/malik-etal-2023-udapter/index.md new file mode 100644 index 0000000..8290e29 --- /dev/null +++ b/content/publication/malik-etal-2023-udapter/index.md @@ -0,0 +1,30 @@ +--- +title: UDAPTER - Efficient Domain Adaptation Using Adapters +authors: +- Bhavitvya Malik +- Abhinav Ramesh Kashyap +- Min-Yen Kan +- Soujanya Poria +date: '2023-05-01' +publishDate: '2024-07-06T02:22:24.610292Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 17th Conference of the European Chapter of the Association + for Computational Linguistics*' +doi: 10.18653/v1/2023.eacl-main.165 +abstract: 'We propose two methods to make unsupervised domain adaptation (UDA) more + parameter efficient using adapters -- small bottleneck layers interspersed with + every layer of the large-scale pre-trained language model (PLM). The first method + deconstructs UDA into a two-step process: first by adding a domain adapter to learn + domain-invariant information and then by adding a task adapter that uses domain-invariant + information to learn task representations in the source domain. The second method + jointly learns a supervised classifier while reducing the divergence measure. Compared + to strong baselines, our simple methods perform well in natural language inference + (MNLI) and the cross-domain sentiment classification task. We even outperform unsupervised + domain adaptation methods such as DANN and DSN in sentiment classification, and + we are within 0.85% F1 for natural language inference task, by fine-tuning only + a fraction of the full model parameters. We release our code at this URL.' +links: +- name: URL + url: https://aclanthology.org/2023.eacl-main.165 +--- diff --git a/content/publication/meng-etal-2023-followupqg/cite.bib b/content/publication/meng-etal-2023-followupqg/cite.bib new file mode 100644 index 0000000..fbd1845 --- /dev/null +++ b/content/publication/meng-etal-2023-followupqg/cite.bib @@ -0,0 +1,22 @@ +@inproceedings{meng-etal-2023-followupqg, + address = {Nusa Dua, Bali}, + author = {Meng, Yan and +Pan, Liangming and +Cao, Yixin and +Kan, Min-Yen}, + booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/2023.ijcnlp-main.17}, + editor = {Park, Jong C. and +Arase, Yuki and +Hu, Baotian and +Lu, Wei and +Wijaya, Derry and +Purwarianti, Ayu and +Krisnadhi, Adila Alfa}, + month = {November}, + pages = {252--271}, + publisher = {Association for Computational Linguistics}, + title = {FollowupQG: Towards information-seeking follow-up question generation}, + url = {https://aclanthology.org/2023.ijcnlp-main.17}, + year = {2023} +} diff --git a/content/publication/meng-etal-2023-followupqg/index.md b/content/publication/meng-etal-2023-followupqg/index.md new file mode 100644 index 0000000..4694bf4 --- /dev/null +++ b/content/publication/meng-etal-2023-followupqg/index.md @@ -0,0 +1,19 @@ +--- +title: 'FollowupQG: Towards information-seeking follow-up question generation' +authors: +- Yan Meng +- Liangming Pan +- Yixin Cao +- Min-Yen Kan +date: '2023-11-01' +publishDate: '2024-07-06T02:22:24.527498Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 13th International Joint Conference on Natural Language + Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association + for Computational Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/2023.ijcnlp-main.17 +links: +- name: URL + url: https://aclanthology.org/2023.ijcnlp-main.17 +--- diff --git a/content/publication/ou-etal-2023-songs/cite.bib b/content/publication/ou-etal-2023-songs/cite.bib new file mode 100644 index 0000000..38e53e6 --- /dev/null +++ b/content/publication/ou-etal-2023-songs/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{ou-etal-2023-songs, + abstract = {The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to prompt-driven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system. Our model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall. In our subjective evaluation, our model shows a 75% relative enhancement on overall quality, compared against naive fine-tuning (Code available at r̆lhttps://github.com/Sonata165/ControllableLyricTranslation).}, + address = {Toronto, Canada}, + author = {Ou, Longshen and +Ma, Xichu and +Kan, Min-Yen and +Wang, Ye}, + booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/2023.acl-long.27}, + editor = {Rogers, Anna and +Boyd-Graber, Jordan and +Okazaki, Naoaki}, + month = {July}, + pages = {447--467}, + publisher = {Association for Computational Linguistics}, + title = {Songs Across Borders: Singable and Controllable Neural Lyric Translation}, + url = {https://aclanthology.org/2023.acl-long.27}, + year = {2023} +} diff --git a/content/publication/ou-etal-2023-songs/index.md b/content/publication/ou-etal-2023-songs/index.md new file mode 100644 index 0000000..473edc5 --- /dev/null +++ b/content/publication/ou-etal-2023-songs/index.md @@ -0,0 +1,29 @@ +--- +title: 'Songs Across Borders: Singable and Controllable Neural Lyric Translation' +authors: +- Longshen Ou +- Xichu Ma +- Min-Yen Kan +- Ye Wang +date: '2023-07-01' +publishDate: '2024-07-06T02:22:24.625493Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 61st Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/2023.acl-long.27 +abstract: The development of general-domain neural machine translation (NMT) methods + has advanced significantly in recent years, but the lack of naturalness and musical + constraints in the outputs makes them unable to produce singable lyric translations. + This paper bridges the singability quality gap by formalizing lyric translation + into a constrained translation problem, converting theoretical guidance and practical + techniques from translatology literature to prompt-driven NMT approaches, exploring + better adaptation methods, and instantiating them to an English-Chinese lyric translation + system. Our model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme + accuracy, and word boundary recall. In our subjective evaluation, our model shows + a 75% relative enhancement on overall quality, compared against naive fine-tuning + (Code available at r̆lhttps://github.com/Sonata165/ControllableLyricTranslation). +links: +- name: URL + url: https://aclanthology.org/2023.acl-long.27 +--- diff --git a/content/publication/pan-etal-2023-attacking/cite.bib b/content/publication/pan-etal-2023-attacking/cite.bib new file mode 100644 index 0000000..4f321e2 --- /dev/null +++ b/content/publication/pan-etal-2023-attacking/cite.bib @@ -0,0 +1,22 @@ +@inproceedings{pan-etal-2023-attacking, + address = {Nusa Dua, Bali}, + author = {Pan, Liangming and +Chen, Wenhu and +Kan, Min-Yen and +Wang, William Yang}, + booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/2023.ijcnlp-main.35}, + editor = {Park, Jong C. and +Arase, Yuki and +Hu, Baotian and +Lu, Wei and +Wijaya, Derry and +Purwarianti, Ayu and +Krisnadhi, Adila Alfa}, + month = {November}, + pages = {525--539}, + publisher = {Association for Computational Linguistics}, + title = {Attacking Open-domain Question Answering by Injecting Misinformation}, + url = {https://aclanthology.org/2023.ijcnlp-main.35}, + year = {2023} +} diff --git a/content/publication/pan-etal-2023-attacking/index.md b/content/publication/pan-etal-2023-attacking/index.md new file mode 100644 index 0000000..38ccbf4 --- /dev/null +++ b/content/publication/pan-etal-2023-attacking/index.md @@ -0,0 +1,19 @@ +--- +title: Attacking Open-domain Question Answering by Injecting Misinformation +authors: +- Liangming Pan +- Wenhu Chen +- Min-Yen Kan +- William Yang Wang +date: '2023-11-01' +publishDate: '2024-07-06T02:22:24.540512Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 13th International Joint Conference on Natural Language + Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association + for Computational Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/2023.ijcnlp-main.35 +links: +- name: URL + url: https://aclanthology.org/2023.ijcnlp-main.35 +--- diff --git a/content/publication/pan-etal-2023-fact/cite.bib b/content/publication/pan-etal-2023-fact/cite.bib new file mode 100644 index 0000000..1f0ab9a --- /dev/null +++ b/content/publication/pan-etal-2023-fact/cite.bib @@ -0,0 +1,22 @@ +@inproceedings{pan-etal-2023-fact, + abstract = {Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning. In this paper, we present Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions. We first leverage the in-context learning ability of large language models to generate reasoning programs to guide the verification process. Afterward, we execute the program by delegating each sub-task to the corresponding sub-task handler. This process makes our model both explanatory and data-efficient, providing clear explanations of its reasoning process and requiring minimal training data. We evaluate ProgramFC on two challenging fact-checking datasets and show that it outperforms seven fact-checking baselines across different settings of evidence availability, with explicit output programs that benefit human debugging. Our codes and data are publicly available at r̆lhttps://github.com/mbzuai-nlp/ProgramFC.}, + address = {Toronto, Canada}, + author = {Pan, Liangming and +Wu, Xiaobao and +Lu, Xinyuan and +Luu, Anh Tuan and +Wang, William Yang and +Kan, Min-Yen and +Nakov, Preslav}, + booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/2023.acl-long.386}, + editor = {Rogers, Anna and +Boyd-Graber, Jordan and +Okazaki, Naoaki}, + month = {July}, + pages = {6981--7004}, + publisher = {Association for Computational Linguistics}, + title = {Fact-Checking Complex Claims with Program-Guided Reasoning}, + url = {https://aclanthology.org/2023.acl-long.386}, + year = {2023} +} diff --git a/content/publication/pan-etal-2023-fact/index.md b/content/publication/pan-etal-2023-fact/index.md new file mode 100644 index 0000000..037166c --- /dev/null +++ b/content/publication/pan-etal-2023-fact/index.md @@ -0,0 +1,34 @@ +--- +title: Fact-Checking Complex Claims with Program-Guided Reasoning +authors: +- Liangming Pan +- Xiaobao Wu +- Xinyuan Lu +- Anh Tuan Luu +- William Yang Wang +- Min-Yen Kan +- Preslav Nakov +date: '2023-07-01' +publishDate: '2024-07-06T02:22:24.639283Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 61st Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/2023.acl-long.386 +abstract: Fact-checking real-world claims often requires collecting multiple pieces + of evidence and applying complex multi-step reasoning. In this paper, we present + Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that decomposes + complex claims into simpler sub-tasks that can be solved using a shared library + of specialized functions. We first leverage the in-context learning ability of large + language models to generate reasoning programs to guide the verification process. + Afterward, we execute the program by delegating each sub-task to the corresponding + sub-task handler. This process makes our model both explanatory and data-efficient, + providing clear explanations of its reasoning process and requiring minimal training + data. We evaluate ProgramFC on two challenging fact-checking datasets and show that + it outperforms seven fact-checking baselines across different settings of evidence + availability, with explicit output programs that benefit human debugging. Our codes + and data are publicly available at r̆lhttps://github.com/mbzuai-nlp/ProgramFC. +links: +- name: URL + url: https://aclanthology.org/2023.acl-long.386 +--- diff --git a/content/publication/pan-etal-2023-investigating/cite.bib b/content/publication/pan-etal-2023-investigating/cite.bib new file mode 100644 index 0000000..210bea9 --- /dev/null +++ b/content/publication/pan-etal-2023-investigating/cite.bib @@ -0,0 +1,21 @@ +@inproceedings{pan-etal-2023-investigating, + address = {Nusa Dua, Bali}, + author = {Pan, Liangming and +Zhang, Yunxiang and +Kan, Min-Yen}, + booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/2023.ijcnlp-main.34}, + editor = {Park, Jong C. and +Arase, Yuki and +Hu, Baotian and +Lu, Wei and +Wijaya, Derry and +Purwarianti, Ayu and +Krisnadhi, Adila Alfa}, + month = {November}, + pages = {511--524}, + publisher = {Association for Computational Linguistics}, + title = {Investigating Zero- and Few-shot Generalization in Fact Verification}, + url = {https://aclanthology.org/2023.ijcnlp-main.34}, + year = {2023} +} diff --git a/content/publication/pan-etal-2023-investigating/index.md b/content/publication/pan-etal-2023-investigating/index.md new file mode 100644 index 0000000..518ef1c --- /dev/null +++ b/content/publication/pan-etal-2023-investigating/index.md @@ -0,0 +1,18 @@ +--- +title: Investigating Zero- and Few-shot Generalization in Fact Verification +authors: +- Liangming Pan +- Yunxiang Zhang +- Min-Yen Kan +date: '2023-11-01' +publishDate: '2024-07-06T02:22:24.534246Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 13th International Joint Conference on Natural Language + Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association + for Computational Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/2023.ijcnlp-main.34 +links: +- name: URL + url: https://aclanthology.org/2023.ijcnlp-main.34 +--- diff --git a/content/publication/pan-etal-2023-qacheck/cite.bib b/content/publication/pan-etal-2023-qacheck/cite.bib new file mode 100644 index 0000000..36cad00 --- /dev/null +++ b/content/publication/pan-etal-2023-qacheck/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{pan-etal-2023-qacheck, + abstract = {Fact-checking real-world claims often requires intricate, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking systems often lack transparency in their decision-making, making it challenging for users to comprehend their reasoning process. To address this, we propose the Question-guided Multi-hop Fact-Checking (QACheck) system, which guides the model′s reasoning process by asking a series of questions critical for verifying a claim. QACheck has five key modules: a claim verifier, a question generator, a question-answering module, a QA validator, and a reasoner. Users can input a claim into QACheck, which then predicts its veracity and provides a comprehensive report detailing its reasoning process, guided by a sequence of (question, answer) pairs. QACheck also provides the source of evidence supporting each question, fostering a transparent, explainable, and user-friendly fact-checking process.}, + address = {Singapore}, + author = {Pan, Liangming and +Lu, Xinyuan and +Kan, Min-Yen and +Nakov, Preslav}, + booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, + doi = {10.18653/v1/2023.emnlp-demo.23}, + editor = {Feng, Yansong and +Lefever, Els}, + month = {December}, + pages = {264--273}, + publisher = {Association for Computational Linguistics}, + title = {QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking}, + url = {https://aclanthology.org/2023.emnlp-demo.23}, + year = {2023} +} diff --git a/content/publication/pan-etal-2023-qacheck/index.md b/content/publication/pan-etal-2023-qacheck/index.md new file mode 100644 index 0000000..2295d4f --- /dev/null +++ b/content/publication/pan-etal-2023-qacheck/index.md @@ -0,0 +1,30 @@ +--- +title: 'QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking' +authors: +- Liangming Pan +- Xinyuan Lu +- Min-Yen Kan +- Preslav Nakov +date: '2023-12-01' +publishDate: '2024-07-06T02:22:24.589500Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2023 Conference on Empirical Methods in Natural + Language Processing: System Demonstrations*' +doi: 10.18653/v1/2023.emnlp-demo.23 +abstract: 'Fact-checking real-world claims often requires intricate, multi-step reasoning + due to the absence of direct evidence to support or refute them. However, existing + fact-checking systems often lack transparency in their decision-making, making it + challenging for users to comprehend their reasoning process. To address this, we + propose the Question-guided Multi-hop Fact-Checking (QACheck) system, which guides + the model′s reasoning process by asking a series of questions critical for verifying + a claim. QACheck has five key modules: a claim verifier, a question generator, a + question-answering module, a QA validator, and a reasoner. Users can input a claim + into QACheck, which then predicts its veracity and provides a comprehensive report + detailing its reasoning process, guided by a sequence of (question, answer) pairs. + QACheck also provides the source of evidence supporting each question, fostering + a transparent, explainable, and user-friendly fact-checking process.' +links: +- name: URL + url: https://aclanthology.org/2023.emnlp-demo.23 +--- diff --git a/content/publication/pan-etal-2023-risk/cite.bib b/content/publication/pan-etal-2023-risk/cite.bib new file mode 100644 index 0000000..8c21118 --- /dev/null +++ b/content/publication/pan-etal-2023-risk/cite.bib @@ -0,0 +1,21 @@ +@inproceedings{pan-etal-2023-risk, + abstract = {We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems. We establish a threat model and simulate potential misuse scenarios, both unintentional and intentional, to assess the extent to which LLMs can be utilized to produce misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation (up to 87%) in the performance of ODQA systems. Moreover, we uncover disparities in the attributes associated with persuading humans and machines, presenting an obstacle to current human-centric approaches to combat misinformation. To mitigate the harm caused by LLM-generated misinformation, we propose three defense strategies: misinformation detection, vigilant prompting, and reader ensemble. These approaches have demonstrated promising results, albeit with certain associated costs. Lastly, we discuss the practicality of utilizing LLMs as automatic misinformation generators and provide relevant resources and code to facilitate future research in this area.}, + address = {Singapore}, + author = {Pan, Yikang and +Pan, Liangming and +Chen, Wenhu and +Nakov, Preslav and +Kan, Min-Yen and +Wang, William}, + booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023}, + doi = {10.18653/v1/2023.findings-emnlp.97}, + editor = {Bouamor, Houda and +Pino, Juan and +Bali, Kalika}, + month = {December}, + pages = {1389--1403}, + publisher = {Association for Computational Linguistics}, + title = {On the Risk of Misinformation Pollution with Large Language Models}, + url = {https://aclanthology.org/2023.findings-emnlp.97}, + year = {2023} +} diff --git a/content/publication/pan-etal-2023-risk/index.md b/content/publication/pan-etal-2023-risk/index.md new file mode 100644 index 0000000..840c7ab --- /dev/null +++ b/content/publication/pan-etal-2023-risk/index.md @@ -0,0 +1,34 @@ +--- +title: On the Risk of Misinformation Pollution with Large Language Models +authors: +- Yikang Pan +- Liangming Pan +- Wenhu Chen +- Preslav Nakov +- Min-Yen Kan +- William Wang +date: '2023-12-01' +publishDate: '2024-07-06T02:22:24.546968Z' +publication_types: +- paper-conference +publication: '*Findings of the Association for Computational Linguistics: EMNLP 2023*' +doi: 10.18653/v1/2023.findings-emnlp.97 +abstract: 'We investigate the potential misuse of modern Large Language Models (LLMs) + for generating credible-sounding misinformation and its subsequent impact on information-intensive + applications, particularly Open-Domain Question Answering (ODQA) systems. We establish + a threat model and simulate potential misuse scenarios, both unintentional and intentional, + to assess the extent to which LLMs can be utilized to produce misinformation. Our + study reveals that LLMs can act as effective misinformation generators, leading + to a significant degradation (up to 87%) in the performance of ODQA systems. Moreover, + we uncover disparities in the attributes associated with persuading humans and machines, + presenting an obstacle to current human-centric approaches to combat misinformation. + To mitigate the harm caused by LLM-generated misinformation, we propose three defense + strategies: misinformation detection, vigilant prompting, and reader ensemble. These + approaches have demonstrated promising results, albeit with certain associated costs. + Lastly, we discuss the practicality of utilizing LLMs as automatic misinformation + generators and provide relevant resources and code to facilitate future research + in this area.' +links: +- name: URL + url: https://aclanthology.org/2023.findings-emnlp.97 +--- diff --git a/content/publication/rohatgi-etal-2023-acl/cite.bib b/content/publication/rohatgi-etal-2023-acl/cite.bib new file mode 100644 index 0000000..c575384 --- /dev/null +++ b/content/publication/rohatgi-etal-2023-acl/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{rohatgi-etal-2023-acl, + abstract = {We present ACL OCL, a scholarly corpus derived from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain. Integrating and enhancing the previous versions of the ACL Anthology, the ACL OCL contributes metadata, PDF files, citation graphs and additional structured full texts with sections, figures, and links to a large knowledge resource (Semantic Scholar). The ACL OCL spans seven decades, containing 73K papers, alongside 210K figures. We spotlight how ACL OCL applies to observe trends in computational linguistics. By detecting paper topics with a supervised neural model, we note that interest in ``Syntax: Tagging, Chunking and Parsing″ is waning and ``Natural Language Generation″ is resurging. Our dataset is available from HuggingFace (https://huggingface.co/datasets/WINGNUS/ACL-OCL).}, + address = {Singapore}, + author = {Rohatgi, Shaurya and +Qin, Yanxia and +Aw, Benjamin and +Unnithan, Niranjana and +Kan, Min-Yen}, + booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing}, + doi = {10.18653/v1/2023.emnlp-main.640}, + editor = {Bouamor, Houda and +Pino, Juan and +Bali, Kalika}, + month = {December}, + pages = {10348--10361}, + publisher = {Association for Computational Linguistics}, + title = {The ACL OCL Corpus: Advancing Open Science in Computational Linguistics}, + url = {https://aclanthology.org/2023.emnlp-main.640}, + year = {2023} +} diff --git a/content/publication/rohatgi-etal-2023-acl/index.md b/content/publication/rohatgi-etal-2023-acl/index.md new file mode 100644 index 0000000..376c79e --- /dev/null +++ b/content/publication/rohatgi-etal-2023-acl/index.md @@ -0,0 +1,29 @@ +--- +title: 'The ACL OCL Corpus: Advancing Open Science in Computational Linguistics' +authors: +- Shaurya Rohatgi +- Yanxia Qin +- Benjamin Aw +- Niranjana Unnithan +- Min-Yen Kan +date: '2023-12-01' +publishDate: '2024-07-06T02:22:24.575377Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2023 Conference on Empirical Methods in Natural + Language Processing*' +doi: 10.18653/v1/2023.emnlp-main.640 +abstract: 'We present ACL OCL, a scholarly corpus derived from the ACL Anthology to + assist Open scientific research in the Computational Linguistics domain. Integrating + and enhancing the previous versions of the ACL Anthology, the ACL OCL contributes + metadata, PDF files, citation graphs and additional structured full texts with sections, + figures, and links to a large knowledge resource (Semantic Scholar). The ACL OCL + spans seven decades, containing 73K papers, alongside 210K figures. We spotlight + how ACL OCL applies to observe trends in computational linguistics. By detecting + paper topics with a supervised neural model, we note that interest in ``Syntax: + Tagging, Chunking and Parsing″ is waning and ``Natural Language Generation″ is resurging. + Our dataset is available from HuggingFace (https://huggingface.co/datasets/WINGNUS/ACL-OCL).' +links: +- name: URL + url: https://aclanthology.org/2023.emnlp-main.640 +--- diff --git a/content/publication/xie-etal-2023-echo/cite.bib b/content/publication/xie-etal-2023-echo/cite.bib new file mode 100644 index 0000000..9627c70 --- /dev/null +++ b/content/publication/xie-etal-2023-echo/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{xie-etal-2023-echo, + abstract = {We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios. ECHo employs real-world human-centric deductive information building on a television crime drama. ECHo requires the Theory-of-Mind (ToM) ability to understand and reason about social interactions based on multimodal information. Using ECHo, we propose a unified Chain-of-Thought (CoT) framework to assess the reasoning capability of current AI systems. Our ToM-enhanced CoT pipeline accommodates various large foundation models in both zero-shot and few-shot visio-linguistic reasoning. We use this framework to scrutinize recent large foundation models such as InstructGPT and MiniGPT-4 on three diagnostic human-centric tasks. Further analysis demonstrates ECHo as a challenging dataset to expose imperfections and inconsistencies in reasoning. Our data and code are publicly available at [https://github.com/YuxiXie/ECHo](https://github.com/YuxiXie/ECHo).}, + address = {Singapore}, + author = {Xie, Yuxi and +Li, Guanzhen and +Kan, Min-Yen}, + booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023}, + doi = {10.18653/v1/2023.findings-emnlp.268}, + editor = {Bouamor, Houda and +Pino, Juan and +Bali, Kalika}, + month = {December}, + pages = {4064--4085}, + publisher = {Association for Computational Linguistics}, + title = {ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning}, + url = {https://aclanthology.org/2023.findings-emnlp.268}, + year = {2023} +} diff --git a/content/publication/xie-etal-2023-echo/index.md b/content/publication/xie-etal-2023-echo/index.md new file mode 100644 index 0000000..3bbb9f3 --- /dev/null +++ b/content/publication/xie-etal-2023-echo/index.md @@ -0,0 +1,29 @@ +--- +title: 'ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric + Reasoning' +authors: +- Yuxi Xie +- Guanzhen Li +- Min-Yen Kan +date: '2023-12-01' +publishDate: '2024-07-06T02:22:24.554194Z' +publication_types: +- paper-conference +publication: '*Findings of the Association for Computational Linguistics: EMNLP 2023*' +doi: 10.18653/v1/2023.findings-emnlp.268 +abstract: We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), + a diagnostic dataset of event causality inference grounded in visio-linguistic social + scenarios. ECHo employs real-world human-centric deductive information building + on a television crime drama. ECHo requires the Theory-of-Mind (ToM) ability to understand + and reason about social interactions based on multimodal information. Using ECHo, + we propose a unified Chain-of-Thought (CoT) framework to assess the reasoning capability + of current AI systems. Our ToM-enhanced CoT pipeline accommodates various large + foundation models in both zero-shot and few-shot visio-linguistic reasoning. We + use this framework to scrutinize recent large foundation models such as InstructGPT + and MiniGPT-4 on three diagnostic human-centric tasks. Further analysis demonstrates + ECHo as a challenging dataset to expose imperfections and inconsistencies in reasoning. + Our data and code are publicly available at [https://github.com/YuxiXie/ECHo](https://github.com/YuxiXie/ECHo). +links: +- name: URL + url: https://aclanthology.org/2023.findings-emnlp.268 +--- diff --git a/content/publication/zhang-etal-2024-nnose/cite.bib b/content/publication/zhang-etal-2024-nnose/cite.bib new file mode 100644 index 0000000..5ef112c --- /dev/null +++ b/content/publication/zhang-etal-2024-nnose/cite.bib @@ -0,0 +1,17 @@ +@inproceedings{zhang-etal-2024-nnose, + abstract = {The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job description datasets, there is a need for systems that handle their diversity well. We tackle the complexity in occupational skill datasets tasks---combining and leveraging multiple datasets for skill extraction, to identify rarely observed skills within a dataset, and overcoming the scarcity of skills across datasets. In particular, we investigate the retrieval-augmentation of language models, employing an external datastore for retrieving similar skills in a dataset-unifying manner. Our proposed method, \textbfNearest \textbfNeighbor \textbfOccupational \textbfSkill \textbfExtraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring skills from other datasets in the datastore. This improves skill extraction \textitwithout additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30% span-F1 in cross-dataset settings.}, + address = {St. Julian′s, Malta}, + author = {Zhang, Mike and +van der Goot, Rob and +Kan, Min-Yen and +Plank, Barbara}, + booktitle = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)}, + editor = {Graham, Yvette and +Purver, Matthew}, + month = {March}, + pages = {589--608}, + publisher = {Association for Computational Linguistics}, + title = {NNOSE: Nearest Neighbor Occupational Skill Extraction}, + url = {https://aclanthology.org/2024.eacl-long.35}, + year = {2024} +} diff --git a/content/publication/zhang-etal-2024-nnose/index.md b/content/publication/zhang-etal-2024-nnose/index.md new file mode 100644 index 0000000..bd58b64 --- /dev/null +++ b/content/publication/zhang-etal-2024-nnose/index.md @@ -0,0 +1,30 @@ +--- +title: 'NNOSE: Nearest Neighbor Occupational Skill Extraction' +authors: +- Mike Zhang +- Rob van der Goot +- Min-Yen Kan +- Barbara Plank +date: '2024-03-01' +publishDate: '2024-07-06T02:22:24.520030Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 18th Conference of the European Chapter of the Association + for Computational Linguistics (Volume 1: Long Papers)*' +abstract: The labor market is changing rapidly, prompting increased interest in the + automatic extraction of occupational skills from text. With the advent of English + benchmark job description datasets, there is a need for systems that handle their + diversity well. We tackle the complexity in occupational skill datasets tasks---combining + and leveraging multiple datasets for skill extraction, to identify rarely observed + skills within a dataset, and overcoming the scarcity of skills across datasets. + In particular, we investigate the retrieval-augmentation of language models, employing + an external datastore for retrieving similar skills in a dataset-unifying manner. + Our proposed method, textbfNearest textbfNeighbor textbfOccupational textbfSkill + textbfExtraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring + skills from other datasets in the datastore. This improves skill extraction textitwithout + additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent + patterns, with substantial gains of up to 30% span-F1 in cross-dataset settings. +links: +- name: URL + url: https://aclanthology.org/2024.eacl-long.35 +--- From e85220ce7b178e3d7f2849765a11ca8435dfaa99 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Sat, 6 Jul 2024 02:40:23 +0000 Subject: [PATCH 09/22] Fix text on front page? --- content/_index.md | 6 +- content/authors/alumnus/_index.md | 2 +- publications.bib | 468 ------------------------------ 3 files changed, 3 insertions(+), 473 deletions(-) diff --git a/content/_index.md b/content/_index.md index 6ef4b49..e8cae81 100644 --- a/content/_index.md +++ b/content/_index.md @@ -8,13 +8,11 @@ sections: - block: hero content: title: | - Wowchemy - Research Group + Web IR / NLP Group (WING) @ NUS image: filename: welcome.jpg text: | -
- The **Web, Information Retrieval / Natural Language Processing Group (WING)** explores the research area of applied language processing and information retrieval to the Web and related technologies. Areas of current interest are question answering, scholarly digital libraries, verb similarity, focused crawling, citation parsing and spidering, web page classification and division, text segmentation, and full text analysis. WING is headed by A/P Min-Yen KAN. We are based in the Computational Linguistics Laboratory of the School of Computing at the National University of Singapore. We often work with the Natural Language Processing Group and the (Lab for Media Search)[http://lms.comp.nus.edu.sg/]. We are part of the Media Technologies research group umbrella. + The Web, Information Retrieval / Natural Language Processing Group (WING) explores the research area of applied language processing and information retrieval to the Web and related technologies. Areas of current interest are question answering, scholarly digital libraries, verb similarity, focused crawling, citation parsing and spidering, web page classification and division, text segmentation, and full text analysis. WING is headed by Min (A/P Min-Yen KAN). We are based in the Computational Linguistics Laboratory of the School of Computing at the National University of Singapore. We often work with the Natural Language Processing Group and the Lab for Media Search. We are part of the Media Technologies research group umbrella. - block: collection content: diff --git a/content/authors/alumnus/_index.md b/content/authors/alumnus/_index.md index 20642e7..1d3a1e9 100644 --- a/content/authors/alumnus/_index.md +++ b/content/authors/alumnus/_index.md @@ -26,5 +26,5 @@ highlight_name: false user_groups: - Alumni ----xw +--- An alumnus \ No newline at end of file diff --git a/publications.bib b/publications.bib index 5081b98..e69de29 100644 --- a/publications.bib +++ b/publications.bib @@ -1,468 +0,0 @@ -@inproceedings{han-etal-2024-self, - title = "Self-Adaptive Sampling for Accurate Video Question Answering on Image Text Models", - author = "Han, Wei and - Chen, Hui and - Kan, Min-Yen and - Poria, Soujanya", - editor = "Duh, Kevin and - Gomez, Helena and - Bethard, Steven", - booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024", - month = jun, - year = "2024", - address = "Mexico City, Mexico", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2024.findings-naacl.162", - pages = "2522--2534", - abstract = "Image{--}text models (ITMs) is the prevalent architecture to solve video question{--}answering tasks, which requires only a few input frames to save huge computational cost compared to video{--}language models.However, we find existent ITM video question{--}answering solutions either 1) adopt simplistic and unintentional sampling strategies, which may miss key frames to offer the answer clues; or 2) sample a large number of frames into divided groups, which the computational sources can not accommodate. In this work, we aim at an efficient sampling method towards the few-frame situations.We first summarize a family of prior sampling methods based on question{--}frame correlation into a unified one, dubbed *Most Implied Frames* (MIF). Through some primary results and analysis, Through analysis, we form a hypothesis that question-aware sampling is not necessary, from which we further propose the other method *Most Dominant Frames* (MDF).Experimental results on four public datasets and three advanced ITMs demonstrate that our proposed strategies can boost the performance for image{--}text pretrained models, and have a wide application scenario in terms of model architectures and dataset types. Our code is available at https://github.com/declare-lab/Sealing\url{https://github.com/declare-lab/Sealing}.", -} - -@inproceedings{li-etal-2024-uno, - title = "{UNO}-{DST}: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking", - author = "Li, Chuang and - Zhang, Yan and - Kan, Min-Yen and - Li, Haizhou", - editor = "Duh, Kevin and - Gomez, Helena and - Bethard, Steven", - booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024", - month = jun, - year = "2024", - address = "Mexico City, Mexico", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2024.findings-naacl.187", - pages = "2972--2983", - abstract = "Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, but ignore unlabelled data in the target domain.We transform zero-shot DST into few-shot DST by utilising such unlabelled data via joint and self-training methods. Our method incorporates auxiliary tasks that generate slot types as inverse prompts for main tasks, creating slot values during joint training. Cycle consistency between these two tasks enables the generation and selection of quality samples in unknown target domains for subsequent fine-tuning. This approach also facilitates automatic label creation, thereby optimizing the training and fine-tuning of DST models. We demonstrate this method{'}s effectiveness on general language models in zero-shot scenarios, improving average joint goal accuracy by 8{\%} across all domains in MultiWOZ.", -} - -@inproceedings{zhang-etal-2024-nnose, - title = "{NNOSE}: Nearest Neighbor Occupational Skill Extraction", - author = "Zhang, Mike and - van der Goot, Rob and - Kan, Min-Yen and - Plank, Barbara", - editor = "Graham, Yvette and - Purver, Matthew", - booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = mar, - year = "2024", - address = "St. Julian{'}s, Malta", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2024.eacl-long.35", - pages = "589--608", - abstract = "The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job description datasets, there is a need for systems that handle their diversity well. We tackle the complexity in occupational skill datasets tasks{---}combining and leveraging multiple datasets for skill extraction, to identify rarely observed skills within a dataset, and overcoming the scarcity of skills across datasets. In particular, we investigate the retrieval-augmentation of language models, employing an external datastore for retrieving similar skills in a dataset-unifying manner. Our proposed method, \textbf{N}earest \textbf{N}eighbor \textbf{O}ccupational \textbf{S}kill \textbf{E}xtraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring skills from other datasets in the datastore. This improves skill extraction \textit{without} additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30{\%} span-F1 in cross-dataset settings.", -} - -@inproceedings{meng-etal-2023-followupqg, - title = "{F}ollowup{QG}: Towards information-seeking follow-up question generation", - author = "Meng, Yan and - Pan, Liangming and - Cao, Yixin and - Kan, Min-Yen", - editor = "Park, Jong C. and - Arase, Yuki and - Hu, Baotian and - Lu, Wei and - Wijaya, Derry and - Purwarianti, Ayu and - Krisnadhi, Adila Alfa", - booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = nov, - year = "2023", - address = "Nusa Dua, Bali", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.ijcnlp-main.17", - doi = "10.18653/v1/2023.ijcnlp-main.17", - pages = "252--271", -} - -@inproceedings{pan-etal-2023-investigating, - title = "Investigating Zero- and Few-shot Generalization in Fact Verification", - author = "Pan, Liangming and - Zhang, Yunxiang and - Kan, Min-Yen", - editor = "Park, Jong C. and - Arase, Yuki and - Hu, Baotian and - Lu, Wei and - Wijaya, Derry and - Purwarianti, Ayu and - Krisnadhi, Adila Alfa", - booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = nov, - year = "2023", - address = "Nusa Dua, Bali", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.ijcnlp-main.34", - doi = "10.18653/v1/2023.ijcnlp-main.34", - pages = "511--524", -} -@inproceedings{pan-etal-2023-attacking, - title = "Attacking Open-domain Question Answering by Injecting Misinformation", - author = "Pan, Liangming and - Chen, Wenhu and - Kan, Min-Yen and - Wang, William Yang", - editor = "Park, Jong C. and - Arase, Yuki and - Hu, Baotian and - Lu, Wei and - Wijaya, Derry and - Purwarianti, Ayu and - Krisnadhi, Adila Alfa", - booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = nov, - year = "2023", - address = "Nusa Dua, Bali", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.ijcnlp-main.35", - doi = "10.18653/v1/2023.ijcnlp-main.35", - pages = "525--539", -} - -@inproceedings{pan-etal-2023-risk, - title = "On the Risk of Misinformation Pollution with Large Language Models", - author = "Pan, Yikang and - Pan, Liangming and - Chen, Wenhu and - Nakov, Preslav and - Kan, Min-Yen and - Wang, William", - editor = "Bouamor, Houda and - Pino, Juan and - Bali, Kalika", - booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023", - month = dec, - year = "2023", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.findings-emnlp.97", - doi = "10.18653/v1/2023.findings-emnlp.97", - pages = "1389--1403", - abstract = "We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems. We establish a threat model and simulate potential misuse scenarios, both unintentional and intentional, to assess the extent to which LLMs can be utilized to produce misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation (up to 87{\%}) in the performance of ODQA systems. Moreover, we uncover disparities in the attributes associated with persuading humans and machines, presenting an obstacle to current human-centric approaches to combat misinformation. To mitigate the harm caused by LLM-generated misinformation, we propose three defense strategies: misinformation detection, vigilant prompting, and reader ensemble. These approaches have demonstrated promising results, albeit with certain associated costs. Lastly, we discuss the practicality of utilizing LLMs as automatic misinformation generators and provide relevant resources and code to facilitate future research in this area.", -} - -@inproceedings{xie-etal-2023-echo, - title = "{ECH}o: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning", - author = "Xie, Yuxi and - Li, Guanzhen and - Kan, Min-Yen", - editor = "Bouamor, Houda and - Pino, Juan and - Bali, Kalika", - booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023", - month = dec, - year = "2023", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.findings-emnlp.268", - doi = "10.18653/v1/2023.findings-emnlp.268", - pages = "4064--4085", - abstract = "We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios. ECHo employs real-world human-centric deductive information building on a television crime drama. ECHo requires the Theory-of-Mind (ToM) ability to understand and reason about social interactions based on multimodal information. Using ECHo, we propose a unified Chain-of-Thought (CoT) framework to assess the reasoning capability of current AI systems. Our ToM-enhanced CoT pipeline accommodates various large foundation models in both zero-shot and few-shot visio-linguistic reasoning. We use this framework to scrutinize recent large foundation models such as InstructGPT and MiniGPT-4 on three diagnostic human-centric tasks. Further analysis demonstrates ECHo as a challenging dataset to expose imperfections and inconsistencies in reasoning. Our data and code are publicly available at [https://github.com/YuxiXie/ECHo](https://github.com/YuxiXie/ECHo).", -} - -@inproceedings{li-etal-2023-coannotating, - title = "{C}o{A}nnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation", - author = "Li, Minzhi and - Shi, Taiwei and - Ziems, Caleb and - Kan, Min-Yen and - Chen, Nancy and - Liu, Zhengyuan and - Yang, Diyi", - editor = "Bouamor, Houda and - Pino, Juan and - Bali, Kalika", - booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", - month = dec, - year = "2023", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.emnlp-main.92", - doi = "10.18653/v1/2023.emnlp-main.92", - pages = "1487--1505", - abstract = "Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many text-annotation tasks, comparable with or even exceeding human annotators. Such LLMs can serve as alternatives for manual annotation, due to lower costs and higher scalability. However, limited work has leveraged LLMs as complementary annotators, nor explored how annotation work is best allocated among humans and LLMs to achieve both quality and cost objectives. We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Under this framework, we utilize uncertainty to estimate LLMs{'} annotation capability. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21{\%} performance improvement over random baseline. For code implementation, see https://github.com/SALT-NLP/CoAnnotating.", -} - -@inproceedings{lu-etal-2023-scitab, - title = "{SCITAB}: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables", - author = "Lu, Xinyuan and - Pan, Liangming and - Liu, Qian and - Nakov, Preslav and - Kan, Min-Yen", - editor = "Bouamor, Houda and - Pino, Juan and - Bali, Kalika", - booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", - month = dec, - year = "2023", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.emnlp-main.483", - doi = "10.18653/v1/2023.emnlp-main.483", - pages = "7787--7813", - abstract = "Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence. We present SCITAB, a challenging evaluation dataset consisting of 1.2K expert-verified scientific claims that 1) originate from authentic scientific publications and 2) require compositional reasoning for verification. The claims are paired with evidence-containing scientific tables annotated with labels. Through extensive evaluations, we demonstrate that SCITAB poses a significant challenge to state-of-the-art models, including table-based pretraining models and large language models. All models except GPT-4 achieved performance barely above random guessing. Popular prompting techniques, such as Chain-of-Thought, do not achieve much performance gains on SCITAB. Our analysis uncovers several unique challenges posed by SCITAB, including table grounding, claim ambiguity, and compositional reasoning. Our codes and data are publicly available at https://github.com/XinyuanLu00/SciTab.", -} - -@inproceedings{rohatgi-etal-2023-acl, - title = "The {ACL} {OCL} Corpus: Advancing Open Science in Computational Linguistics", - author = "Rohatgi, Shaurya and - Qin, Yanxia and - Aw, Benjamin and - Unnithan, Niranjana and - Kan, Min-Yen", - editor = "Bouamor, Houda and - Pino, Juan and - Bali, Kalika", - booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", - month = dec, - year = "2023", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.emnlp-main.640", - doi = "10.18653/v1/2023.emnlp-main.640", - pages = "10348--10361", - abstract = "We present ACL OCL, a scholarly corpus derived from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain. Integrating and enhancing the previous versions of the ACL Anthology, the ACL OCL contributes metadata, PDF files, citation graphs and additional structured full texts with sections, figures, and links to a large knowledge resource (Semantic Scholar). The ACL OCL spans seven decades, containing 73K papers, alongside 210K figures. We spotlight how ACL OCL applies to observe trends in computational linguistics. By detecting paper topics with a supervised neural model, we note that interest in {``}Syntax: Tagging, Chunking and Parsing{''} is waning and {``}Natural Language Generation{''} is resurging. Our dataset is available from HuggingFace (https://huggingface.co/datasets/WINGNUS/ACL-OCL).", -} - - -@inproceedings{diao-etal-2023-doolittle, - title = "Doolittle: Benchmarks and Corpora for Academic Writing Formalization", - author = "Diao, Shizhe and - Lei, Yongyu and - Pan, Liangming and - Fang, Tianqing and - Zhou, Wangchunshu and - Keh, Sedrick and - Kan, Min-Yen and - Zhang, Tong", - editor = "Bouamor, Houda and - Pino, Juan and - Bali, Kalika", - booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", - month = dec, - year = "2023", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.emnlp-main.809", - doi = "10.18653/v1/2023.emnlp-main.809", - pages = "13093--13111", - abstract = "Improving the quality of academic writing is a meaningful but challenging task. Conventional methods of language refinement focus on narrow, specific linguistic features within isolated sentences, such as grammatical errors and improper word use. We propose a more general task, Academic Writing Formalization (AWF), to improve the overall quality of formal academic writing at the paragraph level. We formulate this language refinement task as a formal text style transfer task which transfers informal-academic text to formal-academic and contribute a large-scale non-parallel dataset, Doolittle, for this purpose. Concurrently, we apply a method named metric-oriented reinforcement learning (MORL) to two large language models (LLM) where we incorporate different levels of automatic feedback into the training process. Our experiments reveal that existing text transfer models and grammatical error correction models address certain aspects of AWF but still have a significant performance gap compared to human performance. Meanwhile, language models fine-tuned with our MORL method exhibit considerably improved performance, rivaling the latest chatbot ChatGPT, but still have a non-negligible gap compared to the ground truth formal-academic texts in Doolittle.", -} - - -@inproceedings{pan-etal-2023-qacheck, - title = "{QAC}heck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking", - author = "Pan, Liangming and - Lu, Xinyuan and - Kan, Min-Yen and - Nakov, Preslav", - editor = "Feng, Yansong and - Lefever, Els", - booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", - month = dec, - year = "2023", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.emnlp-demo.23", - doi = "10.18653/v1/2023.emnlp-demo.23", - pages = "264--273", - abstract = "Fact-checking real-world claims often requires intricate, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking systems often lack transparency in their decision-making, making it challenging for users to comprehend their reasoning process. To address this, we propose the Question-guided Multi-hop Fact-Checking (QACheck) system, which guides the model{'}s reasoning process by asking a series of questions critical for verifying a claim. QACheck has five key modules: a claim verifier, a question generator, a question-answering module, a QA validator, and a reasoner. Users can input a claim into QACheck, which then predicts its veracity and provides a comprehensive report detailing its reasoning process, guided by a sequence of (question, answer) pairs. QACheck also provides the source of evidence supporting each question, fostering a transparent, explainable, and user-friendly fact-checking process.", -} - -@inproceedings{ding-etal-2023-cocoscisum, - title = "{C}oco{S}ci{S}um: A Scientific Summarization Toolkit with Compositional Controllability", - author = "Ding, Yixi and - Qin, Yanxia and - Liu, Qian and - Kan, Min-Yen", - editor = "Feng, Yansong and - Lefever, Els", - booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", - month = dec, - year = "2023", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.emnlp-demo.47", - doi = "10.18653/v1/2023.emnlp-demo.47", - pages = "518--526", - abstract = "We present a novel toolkit for controlled summarization of scientific documents, designed for the specific needs of the scientific community. Our system generates summaries based on user preferences, adjusting key attributes specifically of length and keyword inclusion. A distinguishing feature is its ability to manage multiple attributes concurrently, demonstrating Compositional Controllability for Scientific Summarization (CocoSciSum). Benchmarked against the strong Flan-T5 baseline, CocoSciSum exhibits superior performance on both the quality of summaries generated and the control over single and multiple attributes. Moreover, CocoSciSum is a user-centric toolkit, supporting user preferences expressed in natural language instructions, and accommodating diverse input document formats. CocoSciSum is available on GitHub (https://github.com/WING-NUS/SciAssist/tree/CocoSciSum) with an introduction video (https://youtu.be/YC1YDeEjAbQ).", -} - -@inproceedings{benotti-etal-2023-understanding, - title = "Understanding Ethics in {NLP} Authoring and Reviewing", - author = {Benotti, Luciana and - Fort, Kar{\"e}n and - Kan, Min-Yen and - Tsvetkov, Yulia}, - editor = "Zanzotto, Fabio Massimo and - Pradhan, Sameer", - booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts", - month = may, - year = "2023", - address = "Dubrovnik, Croatia", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.eacl-tutorials.4", - doi = "10.18653/v1/2023.eacl-tutorials.4", - pages = "19--24", - abstract = "With NLP research now quickly being transferred into real-world applications, it is important to be aware of and think through the consequences of our scientific investigation. Such ethical considerations are important in both authoring and reviewing. This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues and review common considerations that recur in NLP research. The methodology is interactive and participatory, including case studies and working in groups. Importantly, the participants will be co-building the tutorial outcomes and will be working to create further tutorial materials to share as public outcomes.", -} - -@inproceedings{malik-etal-2023-udapter, - title = "{UDAPTER} - Efficient Domain Adaptation Using Adapters", - author = "Malik, Bhavitvya and - Ramesh Kashyap, Abhinav and - Kan, Min-Yen and - Poria, Soujanya", - editor = "Vlachos, Andreas and - Augenstein, Isabelle", - booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", - month = may, - year = "2023", - address = "Dubrovnik, Croatia", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.eacl-main.165", - doi = "10.18653/v1/2023.eacl-main.165", - pages = "2249--2263", - abstract = "We propose two methods to make unsupervised domain adaptation (UDA) more parameter efficient using adapters {--} small bottleneck layers interspersed with every layer of the large-scale pre-trained language model (PLM). The first method deconstructs UDA into a two-step process: first by adding a domain adapter to learn domain-invariant information and then by adding a task adapter that uses domain-invariant information to learn task representations in the source domain. The second method jointly learns a supervised classifier while reducing the divergence measure. Compared to strong baselines, our simple methods perform well in natural language inference (MNLI) and the cross-domain sentiment classification task. We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0.85{\%} F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters. We release our code at this URL.", -} - - -@inproceedings{chow-etal-2023-travlr, - title = "{T}ra{VLR}: Now You See It, Now You Don{'}t! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoning", - author = "Chow, Keng Ji and - Tan, Samson and - Kan, Min-Yen", - editor = "Vlachos, Andreas and - Augenstein, Isabelle", - booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", - month = may, - year = "2023", - address = "Dubrovnik, Croatia", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.eacl-main.242", - doi = "10.18653/v1/2023.eacl-main.242", - pages = "3322--3347", - abstract = "Numerous visio-linguistic (V+L) representation learning methods have been developed, yet existing datasets do not adequately evaluate the extent to which they represent visual and linguistic concepts in a unified space. We propose several novel evaluation settings for V+L models, including cross-modal transfer. Furthermore, existing V+L benchmarks often report global accuracy scores on the entire dataset, making it difficult to pinpoint the specific reasoning tasks that models fail and succeed at. We present TraVLR, a synthetic dataset comprising four V+L reasoning tasks. TraVLR{'}s synthetic nature allows us to constrain its training and testing distributions along task-relevant dimensions, enabling the evaluation of out-of-distribution generalisation. Each example in TraVLR redundantly encodes the scene in two modalities, allowing either to be dropped or added during training or testing without losing relevant information. We compare the performance of four state-of-the-art V+L models, finding that while they perform well on test examples from the same modality, they all fail at cross-modal transfer and have limited success accommodating the addition or deletion of one modality. We release TraVLR as an open challenge for the research community.", -} - -@inproceedings{ou-etal-2023-songs, - title = "Songs Across Borders: Singable and Controllable Neural Lyric Translation", - author = "Ou, Longshen and - Ma, Xichu and - Kan, Min-Yen and - Wang, Ye", - editor = "Rogers, Anna and - Boyd-Graber, Jordan and - Okazaki, Naoaki", - booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = jul, - year = "2023", - address = "Toronto, Canada", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.acl-long.27", - doi = "10.18653/v1/2023.acl-long.27", - pages = "447--467", - abstract = "The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to prompt-driven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system. Our model achieves 99.85{\%}, 99.00{\%}, and 95.52{\%} on length accuracy, rhyme accuracy, and word boundary recall. In our subjective evaluation, our model shows a 75{\%} relative enhancement on overall quality, compared against naive fine-tuning (Code available at \url{https://github.com/Sonata165/ControllableLyricTranslation}).", -} - -@inproceedings{aksu-etal-2023-prompter, - title = "Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation", - author = "Aksu, Ibrahim Taha and - Kan, Min-Yen and - Chen, Nancy", - editor = "Rogers, Anna and - Boyd-Graber, Jordan and - Okazaki, Naoaki", - booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = jul, - year = "2023", - address = "Toronto, Canada", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.acl-long.252", - doi = "10.18653/v1/2023.acl-long.252", - pages = "4588--4603", - abstract = "A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data {---} zero-shot domain adaptation. Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem due to its robustness. However, it has yet to be applied to the zero-shot scenarios, as it is not clear how to apply it unsupervisedly. Our method, Prompter, uses descriptions of target domain slots to generate dynamic prefixes that are concatenated to the key and values at each layer{'}s self-attention mechanism. This allows for the use of prefix-tuning in zero-shot. Prompter outperforms previous methods on both the MultiWOZ and SGD benchmarks. In generating prefixes, our analyses find that Prompter not only utilizes the semantics of slot descriptions but also how often the slots appear together in conversation. Moreover, Prompter{'}s gains are due to its improved ability to distinguish {''}none{''}-valued dialogue slots, compared against baselines.", -} - -@inproceedings{pan-etal-2023-fact, - title = "Fact-Checking Complex Claims with Program-Guided Reasoning", - author = "Pan, Liangming and - Wu, Xiaobao and - Lu, Xinyuan and - Luu, Anh Tuan and - Wang, William Yang and - Kan, Min-Yen and - Nakov, Preslav", - editor = "Rogers, Anna and - Boyd-Graber, Jordan and - Okazaki, Naoaki", - booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = jul, - year = "2023", - address = "Toronto, Canada", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2023.acl-long.386", - doi = "10.18653/v1/2023.acl-long.386", - pages = "6981--7004", - abstract = "Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning. In this paper, we present Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions. We first leverage the in-context learning ability of large language models to generate reasoning programs to guide the verification process. Afterward, we execute the program by delegating each sub-task to the corresponding sub-task handler. This process makes our model both explanatory and data-efficient, providing clear explanations of its reasoning process and requiring minimal training data. We evaluate ProgramFC on two challenging fact-checking datasets and show that it outperforms seven fact-checking baselines across different settings of evidence availability, with explicit output programs that benefit human debugging. Our codes and data are publicly available at \url{https://github.com/mbzuai-nlp/ProgramFC}.", -} - -@inproceedings{zhang-etal-2022-interpreting, - title = "Interpreting the Robustness of Neural {NLP} Models to Textual Perturbations", - author = "Zhang, Yunxiang and - Pan, Liangming and - Tan, Samson and - Kan, Min-Yen", - editor = "Muresan, Smaranda and - Nakov, Preslav and - Villavicencio, Aline", - booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", - month = may, - year = "2022", - address = "Dublin, Ireland", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2022.findings-acl.315", - doi = "10.18653/v1/2022.findings-acl.315", - pages = "3993--4007", - abstract = "Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear why models are less robust to some perturbations than others. In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence). We further give a causal justification for the learnability metric. We conduct extensive experiments with four prominent NLP models {---} TextRNN, BERT, RoBERTa and XLNet {---} over eight types of textual perturbations on three datasets. We show that a model which is better at identifying a perturbation (higher learnability) becomes worse at ignoring such a perturbation at test time (lower robustness), providing empirical support for our hypothesis.", -} - -@inproceedings{xu-etal-2022-corefdiffs, - title = "{C}oref{D}iffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations", - author = "Xu, Lin and - Zhou, Qixian and - Fu, Jinlan and - Kan, Min-Yen and - Ng, See-Kiong", - editor = "Calzolari, Nicoletta and - Huang, Chu-Ren and - Kim, Hansaem and - Pustejovsky, James and - Wanner, Leo and - Choi, Key-Sun and - Ryu, Pum-Mo and - Chen, Hsin-Hsi and - Donatelli, Lucia and - Ji, Heng and - Kurohashi, Sadao and - Paggio, Patrizia and - Xue, Nianwen and - Kim, Seokhwan and - Hahm, Younggyun and - He, Zhong and - Lee, Tony Kyungil and - Santus, Enrico and - Bond, Francis and - Na, Seung-Hoon", - booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", - month = oct, - year = "2022", - address = "Gyeongju, Republic of Korea", - publisher = "International Committee on Computational Linguistics", - url = "https://aclanthology.org/2022.coling-1.38", - pages = "471--484", - abstract = "Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5{\%}, 7.4{\%} and 8.2{\%} on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation.", -} From 63cacfa9c93a0fef7ab9bacc781504700f74701e Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Sat, 6 Jul 2024 05:55:24 +0000 Subject: [PATCH 10/22] Link min as an author --- content/publication/aksu-etal-2021-velocidapter/index.md | 2 +- content/publication/aksu-etal-2022-n/index.md | 2 +- content/publication/aksu-etal-2023-prompter/index.md | 2 +- content/publication/benotti-etal-2023-understanding/index.md | 2 +- content/publication/chow-etal-2023-travlr/index.md | 2 +- content/publication/diao-etal-2023-doolittle/index.md | 2 +- content/publication/ding-etal-2023-cocoscisum/index.md | 2 +- content/publication/li-etal-2023-coannotating/index.md | 2 +- content/publication/li-etal-2024-uno/index.md | 2 +- content/publication/lu-etal-2023-scitab/index.md | 2 +- content/publication/malik-etal-2023-udapter/index.md | 2 +- content/publication/meng-etal-2023-followupqg/index.md | 2 +- content/publication/ou-etal-2023-songs/index.md | 2 +- content/publication/pan-etal-2023-attacking/index.md | 2 +- content/publication/pan-etal-2023-fact/index.md | 2 +- content/publication/pan-etal-2023-investigating/index.md | 2 +- content/publication/pan-etal-2023-risk/index.md | 2 +- content/publication/qin-etal-2022-gl/index.md | 2 +- content/publication/ramesh-kashyap-etal-2022-different/index.md | 2 +- content/publication/rohatgi-etal-2023-acl/index.md | 2 +- content/publication/xie-etal-2023-echo/index.md | 2 +- content/publication/xu-etal-2022-corefdiffs/index.md | 2 +- content/publication/zhang-etal-2022-interpreting/index.md | 2 +- content/publication/zhang-etal-2024-nnose/index.md | 2 +- 24 files changed, 24 insertions(+), 24 deletions(-) diff --git a/content/publication/aksu-etal-2021-velocidapter/index.md b/content/publication/aksu-etal-2021-velocidapter/index.md index f1cbd94..2b07848 100644 --- a/content/publication/aksu-etal-2021-velocidapter/index.md +++ b/content/publication/aksu-etal-2021-velocidapter/index.md @@ -4,7 +4,7 @@ title: 'Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synt authors: - Ibrahim Taha Aksu - Zhengyuan Liu -- min +- min   - Nancy Chen date: '2021-07-01' publishDate: '2024-07-05T17:09:42.645613Z' diff --git a/content/publication/aksu-etal-2022-n/index.md b/content/publication/aksu-etal-2022-n/index.md index 30bb6fb..230eec6 100644 --- a/content/publication/aksu-etal-2022-n/index.md +++ b/content/publication/aksu-etal-2022-n/index.md @@ -3,7 +3,7 @@ title: N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking authors: - Ibrahim Aksu - Zhengyuan Liu -- Min-Yen Kan +- min - Nancy Chen date: '2022-05-01' publishDate: '2024-07-05T17:09:42.588862Z' diff --git a/content/publication/aksu-etal-2023-prompter/index.md b/content/publication/aksu-etal-2023-prompter/index.md index c26f736..f1ba6cd 100644 --- a/content/publication/aksu-etal-2023-prompter/index.md +++ b/content/publication/aksu-etal-2023-prompter/index.md @@ -2,7 +2,7 @@ title: 'Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation' authors: - Ibrahim Taha Aksu -- Min-Yen Kan +- min - Nancy Chen date: '2023-07-01' publishDate: '2024-07-06T02:22:24.632344Z' diff --git a/content/publication/benotti-etal-2023-understanding/index.md b/content/publication/benotti-etal-2023-understanding/index.md index d4dc97b..66cd2b2 100644 --- a/content/publication/benotti-etal-2023-understanding/index.md +++ b/content/publication/benotti-etal-2023-understanding/index.md @@ -3,7 +3,7 @@ title: Understanding Ethics in NLP Authoring and Reviewing authors: - Luciana Benotti - Karën Fort -- Min-Yen Kan +- min - Yulia Tsvetkov date: '2023-05-01' publishDate: '2024-07-06T02:22:24.603387Z' diff --git a/content/publication/chow-etal-2023-travlr/index.md b/content/publication/chow-etal-2023-travlr/index.md index fb2681d..97ce7c8 100644 --- a/content/publication/chow-etal-2023-travlr/index.md +++ b/content/publication/chow-etal-2023-travlr/index.md @@ -4,7 +4,7 @@ title: 'TraVLR: Now You See It, Now You Don′t! A Bimodal Dataset for Evaluatin authors: - Keng Ji Chow - Samson Tan -- Min-Yen Kan +- min date: '2023-05-01' publishDate: '2024-07-06T02:22:24.618335Z' publication_types: diff --git a/content/publication/diao-etal-2023-doolittle/index.md b/content/publication/diao-etal-2023-doolittle/index.md index 9f6751a..a81512a 100644 --- a/content/publication/diao-etal-2023-doolittle/index.md +++ b/content/publication/diao-etal-2023-doolittle/index.md @@ -7,7 +7,7 @@ authors: - Tianqing Fang - Wangchunshu Zhou - Sedrick Keh -- Min-Yen Kan +- min - Tong Zhang date: '2023-12-01' publishDate: '2024-07-06T02:22:24.582277Z' diff --git a/content/publication/ding-etal-2023-cocoscisum/index.md b/content/publication/ding-etal-2023-cocoscisum/index.md index 97b3503..a4cdd25 100644 --- a/content/publication/ding-etal-2023-cocoscisum/index.md +++ b/content/publication/ding-etal-2023-cocoscisum/index.md @@ -4,7 +4,7 @@ authors: - Yixi Ding - Yanxia Qin - Qian Liu -- Min-Yen Kan +- min date: '2023-12-01' publishDate: '2024-07-06T02:22:24.596464Z' publication_types: diff --git a/content/publication/li-etal-2023-coannotating/index.md b/content/publication/li-etal-2023-coannotating/index.md index d101c50..b0244dc 100644 --- a/content/publication/li-etal-2023-coannotating/index.md +++ b/content/publication/li-etal-2023-coannotating/index.md @@ -5,7 +5,7 @@ authors: - Minzhi Li - Taiwei Shi - Caleb Ziems -- Min-Yen Kan +- min - Nancy Chen - Zhengyuan Liu - Diyi Yang diff --git a/content/publication/li-etal-2024-uno/index.md b/content/publication/li-etal-2024-uno/index.md index 945d0a0..0f25488 100644 --- a/content/publication/li-etal-2024-uno/index.md +++ b/content/publication/li-etal-2024-uno/index.md @@ -3,7 +3,7 @@ title: 'UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking authors: - Chuang Li - Yan Zhang -- Min-Yen Kan +- min - Haizhou Li date: '2024-06-01' publishDate: '2024-07-06T02:22:24.510368Z' diff --git a/content/publication/lu-etal-2023-scitab/index.md b/content/publication/lu-etal-2023-scitab/index.md index 918745c..b42e6c3 100644 --- a/content/publication/lu-etal-2023-scitab/index.md +++ b/content/publication/lu-etal-2023-scitab/index.md @@ -6,7 +6,7 @@ authors: - Liangming Pan - Qian Liu - Preslav Nakov -- Min-Yen Kan +- min date: '2023-12-01' publishDate: '2024-07-06T02:22:24.568376Z' publication_types: diff --git a/content/publication/malik-etal-2023-udapter/index.md b/content/publication/malik-etal-2023-udapter/index.md index 8290e29..2d5646a 100644 --- a/content/publication/malik-etal-2023-udapter/index.md +++ b/content/publication/malik-etal-2023-udapter/index.md @@ -3,7 +3,7 @@ title: UDAPTER - Efficient Domain Adaptation Using Adapters authors: - Bhavitvya Malik - Abhinav Ramesh Kashyap -- Min-Yen Kan +- min - Soujanya Poria date: '2023-05-01' publishDate: '2024-07-06T02:22:24.610292Z' diff --git a/content/publication/meng-etal-2023-followupqg/index.md b/content/publication/meng-etal-2023-followupqg/index.md index 4694bf4..a12f5ef 100644 --- a/content/publication/meng-etal-2023-followupqg/index.md +++ b/content/publication/meng-etal-2023-followupqg/index.md @@ -4,7 +4,7 @@ authors: - Yan Meng - Liangming Pan - Yixin Cao -- Min-Yen Kan +- min date: '2023-11-01' publishDate: '2024-07-06T02:22:24.527498Z' publication_types: diff --git a/content/publication/ou-etal-2023-songs/index.md b/content/publication/ou-etal-2023-songs/index.md index 473edc5..0a0ffeb 100644 --- a/content/publication/ou-etal-2023-songs/index.md +++ b/content/publication/ou-etal-2023-songs/index.md @@ -3,7 +3,7 @@ title: 'Songs Across Borders: Singable and Controllable Neural Lyric Translation authors: - Longshen Ou - Xichu Ma -- Min-Yen Kan +- min - Ye Wang date: '2023-07-01' publishDate: '2024-07-06T02:22:24.625493Z' diff --git a/content/publication/pan-etal-2023-attacking/index.md b/content/publication/pan-etal-2023-attacking/index.md index 38ccbf4..32960c9 100644 --- a/content/publication/pan-etal-2023-attacking/index.md +++ b/content/publication/pan-etal-2023-attacking/index.md @@ -3,7 +3,7 @@ title: Attacking Open-domain Question Answering by Injecting Misinformation authors: - Liangming Pan - Wenhu Chen -- Min-Yen Kan +- min - William Yang Wang date: '2023-11-01' publishDate: '2024-07-06T02:22:24.540512Z' diff --git a/content/publication/pan-etal-2023-fact/index.md b/content/publication/pan-etal-2023-fact/index.md index 037166c..c494c71 100644 --- a/content/publication/pan-etal-2023-fact/index.md +++ b/content/publication/pan-etal-2023-fact/index.md @@ -6,7 +6,7 @@ authors: - Xinyuan Lu - Anh Tuan Luu - William Yang Wang -- Min-Yen Kan +- min - Preslav Nakov date: '2023-07-01' publishDate: '2024-07-06T02:22:24.639283Z' diff --git a/content/publication/pan-etal-2023-investigating/index.md b/content/publication/pan-etal-2023-investigating/index.md index 518ef1c..915a148 100644 --- a/content/publication/pan-etal-2023-investigating/index.md +++ b/content/publication/pan-etal-2023-investigating/index.md @@ -3,7 +3,7 @@ title: Investigating Zero- and Few-shot Generalization in Fact Verification authors: - Liangming Pan - Yunxiang Zhang -- Min-Yen Kan +- min date: '2023-11-01' publishDate: '2024-07-06T02:22:24.534246Z' publication_types: diff --git a/content/publication/pan-etal-2023-risk/index.md b/content/publication/pan-etal-2023-risk/index.md index 840c7ab..1ec72d6 100644 --- a/content/publication/pan-etal-2023-risk/index.md +++ b/content/publication/pan-etal-2023-risk/index.md @@ -5,7 +5,7 @@ authors: - Liangming Pan - Wenhu Chen - Preslav Nakov -- Min-Yen Kan +- min - William Wang date: '2023-12-01' publishDate: '2024-07-06T02:22:24.546968Z' diff --git a/content/publication/qin-etal-2022-gl/index.md b/content/publication/qin-etal-2022-gl/index.md index aa0e363..049f37f 100644 --- a/content/publication/qin-etal-2022-gl/index.md +++ b/content/publication/qin-etal-2022-gl/index.md @@ -8,7 +8,7 @@ authors: - Qixin Li - Jian-Guang Lou - Wanxiang Che -- Min-Yen Kan +- min date: '2022-05-01' publishDate: '2024-07-05T17:09:42.638128Z' publication_types: diff --git a/content/publication/ramesh-kashyap-etal-2022-different/index.md b/content/publication/ramesh-kashyap-etal-2022-different/index.md index a29d15a..2ac3644 100644 --- a/content/publication/ramesh-kashyap-etal-2022-different/index.md +++ b/content/publication/ramesh-kashyap-etal-2022-different/index.md @@ -3,7 +3,7 @@ title: So Different Yet So Alike! Constrained Unsupervised Text Style Transfer authors: - Abhinav Ramesh Kashyap - Devamanyu Hazarika -- Min-Yen Kan +- min - Roger Zimmermann - Soujanya Poria date: '2022-05-01' diff --git a/content/publication/rohatgi-etal-2023-acl/index.md b/content/publication/rohatgi-etal-2023-acl/index.md index 376c79e..4f0d52b 100644 --- a/content/publication/rohatgi-etal-2023-acl/index.md +++ b/content/publication/rohatgi-etal-2023-acl/index.md @@ -5,7 +5,7 @@ authors: - Yanxia Qin - Benjamin Aw - Niranjana Unnithan -- Min-Yen Kan +- min date: '2023-12-01' publishDate: '2024-07-06T02:22:24.575377Z' publication_types: diff --git a/content/publication/xie-etal-2023-echo/index.md b/content/publication/xie-etal-2023-echo/index.md index 3bbb9f3..8cc1a05 100644 --- a/content/publication/xie-etal-2023-echo/index.md +++ b/content/publication/xie-etal-2023-echo/index.md @@ -4,7 +4,7 @@ title: 'ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human authors: - Yuxi Xie - Guanzhen Li -- Min-Yen Kan +- min date: '2023-12-01' publishDate: '2024-07-06T02:22:24.554194Z' publication_types: diff --git a/content/publication/xu-etal-2022-corefdiffs/index.md b/content/publication/xu-etal-2022-corefdiffs/index.md index 1cc91ee..6b8f10b 100644 --- a/content/publication/xu-etal-2022-corefdiffs/index.md +++ b/content/publication/xu-etal-2022-corefdiffs/index.md @@ -5,7 +5,7 @@ authors: - Lin Xu - Qixian Zhou - Jinlan Fu -- Min-Yen Kan +- min - See-Kiong Ng date: '2022-10-01' publishDate: '2024-07-05T17:09:42.624059Z' diff --git a/content/publication/zhang-etal-2022-interpreting/index.md b/content/publication/zhang-etal-2022-interpreting/index.md index f5534f5..1ab5b8d 100644 --- a/content/publication/zhang-etal-2022-interpreting/index.md +++ b/content/publication/zhang-etal-2022-interpreting/index.md @@ -4,7 +4,7 @@ authors: - Yunxiang Zhang - Liangming Pan - Samson Tan -- Min-Yen Kan +- min date: '2022-05-01' publishDate: '2024-07-05T17:09:42.596004Z' publication_types: diff --git a/content/publication/zhang-etal-2024-nnose/index.md b/content/publication/zhang-etal-2024-nnose/index.md index bd58b64..b8c17dc 100644 --- a/content/publication/zhang-etal-2024-nnose/index.md +++ b/content/publication/zhang-etal-2024-nnose/index.md @@ -3,7 +3,7 @@ title: 'NNOSE: Nearest Neighbor Occupational Skill Extraction' authors: - Mike Zhang - Rob van der Goot -- Min-Yen Kan +- min - Barbara Plank date: '2024-03-01' publishDate: '2024-07-06T02:22:24.520030Z' From ae6b30136f07098804eaa59c83cdfbfe0cd4a4e0 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Thu, 11 Jul 2024 07:40:31 +0000 Subject: [PATCH 11/22] Adding addiional docs from anthology.bib --- publications.bib | 1247 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1247 insertions(+) diff --git a/publications.bib b/publications.bib index e69de29..89f5135 100644 --- a/publications.bib +++ b/publications.bib @@ -0,0 +1,1247 @@ + +@inproceedings{ramesh-kashyap-etal-2021-analyzing, + title = "Analyzing the Domain Robustness of Pretrained Language Models, Layer by Layer", + author = "Ramesh Kashyap, Abhinav and + Mehnaz, Laiba and + Malik, Bhavitvya and + Waheed, Abdul and + Hazarika, Devamanyu and + Kan, Min-Yen and + Shah, Rajiv Ratn", + editor = "Ben-David, Eyal and + Cohen, Shay and + McDonald, Ryan and + Plank, Barbara and + Reichart, Roi and + Rotman, Guy and + Ziser, Yftah", + booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP", + month = apr, + year = "2021", + address = "Kyiv, Ukraine", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.adaptnlp-1.23", + pages = "222--244", + abstract = "The robustness of pretrained language models(PLMs) is generally measured using performance drops on two or more domains. However, we do not yet understand the inherent robustness achieved by contributions from different layers of a PLM. We systematically analyze the robustness of these representations layer by layer from two perspectives. First, we measure the robustness of representations by using domain divergence between two domains. We find that i) Domain variance increases from the lower to the upper layers for vanilla PLMs; ii) Models continuously pretrained on domain-specific data (DAPT)(Gururangan et al., 2020) exhibit more variance than their pretrained PLM counterparts; and that iii) Distilled models (e.g., DistilBERT) also show greater domain variance. Second, we investigate the robustness of representations by analyzing the encoded syntactic and semantic information using diagnostic probes. We find that similar layers have similar amounts of linguistic information for data from an unseen domain.", +} + + +@inproceedings{pan-etal-2021-unsupervised, + title = "Unsupervised Multi-hop Question Answering by Question Generation", + author = "Pan, Liangming and + Chen, Wenhu and + Xiong, Wenhan and + Kan, Min-Yen and + Wang, William Yang", + editor = "Toutanova, Kristina and + Rumshisky, Anna and + Zettlemoyer, Luke and + Hakkani-Tur, Dilek and + Beltagy, Iz and + Bethard, Steven and + Cotterell, Ryan and + Chakraborty, Tanmoy and + Zhou, Yichao", + booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", + month = jun, + year = "2021", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.naacl-main.469", + doi = "10.18653/v1/2021.naacl-main.469", + pages = "5866--5880", + abstract = "Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. Using only generated training data, we can train a competent multi-hop QA which achieves 61{\%} and 83{\%} of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data. Our codes are publicly available at \url{https://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA}.", +} + + +@inproceedings{pan-etal-2021-zero, + title = "Zero-shot Fact Verification by Claim Generation", + author = "Pan, Liangming and + Chen, Wenhu and + Xiong, Wenhan and + Kan, Min-Yen and + Wang, William Yang", + editor = "Zong, Chengqing and + Xia, Fei and + Li, Wenjie and + Navigli, Roberto", + booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)", + month = aug, + year = "2021", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.acl-short.61", + doi = "10.18653/v1/2021.acl-short.61", + pages = "476--483", + abstract = "Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model{'}s F1 from 50{\%} to 77{\%}, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.", +} + + +@inproceedings{tan-etal-2021-reliability, + title = "Reliability Testing for Natural Language Processing Systems", + author = "Tan, Samson and + Joty, Shafiq and + Baxter, Kathy and + Taeihagh, Araz and + Bennett, Gregory A. and + Kan, Min-Yen", + editor = "Zong, Chengqing and + Xia, Fei and + Li, Wenjie and + Navigli, Roberto", + booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", + month = aug, + year = "2021", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.acl-long.321", + doi = "10.18653/v1/2021.acl-long.321", + pages = "4153--4169", + abstract = "Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that reliability testing {---} with an emphasis on interdisciplinary collaboration {---} will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.", +} + + +@inproceedings{ramesh-kashyap-kan-2020-sciwing, + title = "{S}ci{WING}{--} A Software Toolkit for Scientific Document Processing", + author = "Ramesh Kashyap, Abhinav and + Kan, Min-Yen", + editor = "Chandrasekaran, Muthu Kumar and + de Waard, Anita and + Feigenblat, Guy and + Freitag, Dayne and + Ghosal, Tirthankar and + Hovy, Eduard and + Knoth, Petr and + Konopnicki, David and + Mayr, Philipp and + Patton, Robert M. and + Shmueli-Scheuer, Michal", + booktitle = "Proceedings of the First Workshop on Scholarly Document Processing", + month = nov, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.sdp-1.13", + doi = "10.18653/v1/2020.sdp-1.13", + pages = "113--120", + abstract = "We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from \url{http://sciwing.io} and the demos are available at \url{http://rebrand.ly/sciwing-demo}.", +} + + +@inproceedings{tan-etal-2020-mind, + title = "Mind Your Inflections! {I}mproving {NLP} for Non-Standard {E}nglishes with {B}ase-{I}nflection {E}ncoding", + author = "Tan, Samson and + Joty, Shafiq and + Varshney, Lav and + Kan, Min-Yen", + editor = "Webber, Bonnie and + Cohn, Trevor and + He, Yulan and + Liu, Yang", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", + month = nov, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.emnlp-main.455", + doi = "10.18653/v1/2020.emnlp-main.455", + pages = "5647--5663", + abstract = "Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English. Although comprehension by human readers is usually unimpaired by non-standard inflections, current NLP systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols. Fine-tuning pretrained NLP models for downstream tasks using our encoding defends against inflectional adversaries while maintaining performance on clean data. Models using BITE generalize better to dialects with non-standard inflections without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers. Since there has been no prior work on quantitatively evaluating vocabulary efficiency, we propose metrics to do so.", +} + + +@inproceedings{lei-etal-2020-examining, + title = "Re-examining the Role of Schema Linking in Text-to-{SQL}", + author = "Lei, Wenqiang and + Wang, Weixin and + Ma, Zhixin and + Gan, Tian and + Lu, Wei and + Kan, Min-Yen and + Chua, Tat-Seng", + editor = "Webber, Bonnie and + Cohn, Trevor and + He, Yulan and + Liu, Yang", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", + month = nov, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.emnlp-main.564", + doi = "10.18653/v1/2020.emnlp-main.564", + pages = "6943--6954", + abstract = "In existing sophisticated text-to-SQL models, schema linking is often considered as a simple, minor component, belying its importance. By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking. We also build a simple BERT-based baseline, called Schema-Linking SQL (SLSQL) to perform a data-driven study. We find when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity. Many remaining errors are attributable to corpus noise. This suggests schema linking is the crux for the current text-to-SQL task. Our analytic studies provide insights on the characteristics of schema linking for future developments of text-to-SQL tasks.", +} + + +@inproceedings{xie-etal-2020-exploring, + title = "Exploring Question-Specific Rewards for Generating Deep Questions", + author = "Xie, Yuxi and + Pan, Liangming and + Wang, Dongzhe and + Kan, Min-Yen and + Feng, Yansong", + editor = "Scott, Donia and + Bel, Nuria and + Zong, Chengqing", + booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", + month = dec, + year = "2020", + address = "Barcelona, Spain (Online)", + publisher = "International Committee on Computational Linguistics", + url = "https://aclanthology.org/2020.coling-main.228", + doi = "10.18653/v1/2020.coling-main.228", + pages = "2534--2546", + abstract = "Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log likelihood of ground-truth questions using teacher forcing. However, this training objective is inconsistent with actual question quality, which is often reflected by certain global properties such as whether the question can be answered by the document. As such, we directly optimize for QG-specific objectives via reinforcement learning to improve question quality. We design three different rewards that target to improve the fluency, relevance, and answerability of generated questions. We conduct both automatic and human evaluations in addition to thorough analysis to explore the effect of each QG-specific reward. We find that optimizing on question-specific rewards generally leads to better performance in automatic evaluation metrics. However, only the rewards that correlate well with human judgement (e.g., relevance) lead to real improvement in question quality. Optimizing for the others, especially answerability, introduces incorrect bias to the model, resulting in poorer question quality. The code is publicly available at \url{https://github.com/YuxiXie/RL-for-Question-Generation}.", +} + + +@inproceedings{li-etal-2020-molweni, + title = "Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure", + author = "Li, Jiaqi and + Liu, Ming and + Kan, Min-Yen and + Zheng, Zihao and + Wang, Zekun and + Lei, Wenqiang and + Liu, Ting and + Qin, Bing", + editor = "Scott, Donia and + Bel, Nuria and + Zong, Chengqing", + booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", + month = dec, + year = "2020", + address = "Barcelona, Spain (Online)", + publisher = "International Committee on Computational Linguistics", + url = "https://aclanthology.org/2020.coling-main.238", + doi = "10.18653/v1/2020.coling-main.238", + pages = "2642--2652", + abstract = "Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni{'}s source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions. Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing. Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7{\%} F1 on Molweni{'}s questions, a 20+{\%} significant drop as compared against its SQuAD 2.0 performance.", +} + + +@inproceedings{bhola-etal-2020-retrieving, + title = "Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework", + author = "Bhola, Akshay and + Halder, Kishaloy and + Prasad, Animesh and + Kan, Min-Yen", + editor = "Scott, Donia and + Bel, Nuria and + Zong, Chengqing", + booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", + month = dec, + year = "2020", + address = "Barcelona, Spain (Online)", + publisher = "International Committee on Computational Linguistics", + url = "https://aclanthology.org/2020.coling-main.513", + doi = "10.18653/v1/2020.coling-main.513", + pages = "5832--5842", + abstract = "We introduce a deep learning model to learn the set of enumerated job skills associated with a job description. In our analysis of a large-scale government job portal mycareersfuture.sg, we observe that as much as 65{\%} of job descriptions miss describing a significant number of relevant skills. Our model addresses this task from the perspective of an extreme multi-label classification (XMLC) problem, where descriptions are the evidence for the binary relevance of thousands of individual skills. Building upon the current state-of-the-art language modeling approaches such as BERT, we show our XMLC method improves on an existing baseline solution by over 9{\%} and 7{\%} absolute improvements in terms of recall and normalized discounted cumulative gain. We further show that our approach effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings by taking into account the structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process. We further show that our approach, to ensure the BERT-XMLC model accounts for structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process, effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings. To facilitate future research and replication of our work, we have made the dataset and the implementation of our model publicly available.", +} + + +@inproceedings{cao-etal-2020-expertise, + title = "Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen", + author = "Cao, Yixin and + Shui, Ruihao and + Pan, Liangming and + Kan, Min-Yen and + Liu, Zhiyuan and + Chua, Tat-Seng", + editor = "Jurafsky, Dan and + Chai, Joyce and + Schluter, Natalie and + Tetreault, Joel", + booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", + month = jul, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.acl-main.100", + doi = "10.18653/v1/2020.acl-main.100", + pages = "1061--1071", + abstract = "The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at \url{https://srhthu.github.io/expertise-style-transfer/}.", +} + + +@inproceedings{pan-etal-2020-semantic, + title = "Semantic Graphs for Generating Deep Questions", + author = "Pan, Liangming and + Xie, Yuxi and + Feng, Yansong and + Chua, Tat-Seng and + Kan, Min-Yen", + editor = "Jurafsky, Dan and + Chai, Joyce and + Schluter, Natalie and + Tetreault, Joel", + booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", + month = jul, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.acl-main.135", + doi = "10.18653/v1/2020.acl-main.135", + pages = "1463--1475", + abstract = "This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information about the input passage. In order to capture the global structure of the document and facilitate reasoning, we propose a novel framework that first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN). Afterward, we fuse the document-level and graph-level representations to perform joint training of content selection and question decoding. On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance. The code is publicly available at \url{https://github.com/WING-NUS/SG-Deep-Question-Generation}.", +} + + +@inproceedings{tan-etal-2020-morphin, + title = "It{'}s Morphin{'} Time! {C}ombating Linguistic Discrimination with Inflectional Perturbations", + author = "Tan, Samson and + Joty, Shafiq and + Kan, Min-Yen and + Socher, Richard", + editor = "Jurafsky, Dan and + Chai, Joyce and + Schluter, Natalie and + Tetreault, Joel", + booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", + month = jul, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.acl-main.263", + doi = "10.18653/v1/2020.acl-main.263", + pages = "2920--2935", + abstract = "Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.", +} + + +@inproceedings{prasad-kan-2019-glocal, + title = "{G}local: Incorporating Global Information in Local Convolution for Keyphrase Extraction", + author = "Prasad, Animesh and + Kan, Min-Yen", + editor = "Burstein, Jill and + Doran, Christy and + Solorio, Thamar", + booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", + month = jun, + year = "2019", + address = "Minneapolis, Minnesota", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/N19-1182", + doi = "10.18653/v1/N19-1182", + pages = "1837--1846", + abstract = "Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures. While such methods model nodes{'} local pairwise importance, they lack the capability to model global importance relative to other nodes of the graph. This causes such models to miss critical information in tasks where global ranking is a key component for the task, such as in keyphrase extraction. We address this shortcoming by allowing the proper incorporation of global information into the GCN family of models through the use of scaled node weights. In the context of keyphrase extraction, incorporating global random walk scores obtained from TextRank boosts performance significantly. With our proposed method, we achieve state-of-the-art results, bettering a strong baseline by an absolute 2{\%} increase in F1 score.", +} + + +@inproceedings{halder-etal-2019-predicting, + title = "Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture", + author = "Halder, Kishaloy and + Kan, Min-Yen and + Sugiyama, Kazunari", + editor = "Burstein, Jill and + Doran, Christy and + Solorio, Thamar", + booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", + month = jun, + year = "2019", + address = "Minneapolis, Minnesota", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/N19-1318", + doi = "10.18653/v1/N19-1318", + pages = "3148--3157", + abstract = "Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or by sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread to help users comprehend long running discussion threads, which often contain repetitive or irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.", +} + + +@inproceedings{si-etal-2019-sentiment, + title = "Sentiment Aware Neural Machine Translation", + author = "Si, Chenglei and + Wu, Kui and + Aw, Ai Ti and + Kan, Min-Yen", + editor = "Nakazawa, Toshiaki and + Ding, Chenchen and + Dabre, Raj and + Kunchukuttan, Anoop and + Doi, Nobushige and + Oda, Yusuke and + Bojar, Ond{\v{r}}ej and + Parida, Shantipriya and + Goto, Isao and + Mino, Hidaya", + booktitle = "Proceedings of the 6th Workshop on Asian Translation", + month = nov, + year = "2019", + address = "Hong Kong, China", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/D19-5227", + doi = "10.18653/v1/D19-5227", + pages = "200--206", + abstract = "Sentiment ambiguous lexicons refer to words where their polarity depends strongly on con- text. As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data. While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence. We investigate the translation variation in two sentiment scenarios. We perform experiments to study the preservation of sentiment during translation with three different methods that we propose. We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods. We show that NMT can generate both positive- and negative-valent translations of a source sentence, based on a given input sentiment label. Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.", +} + + +@inproceedings{nguyen-etal-2018-treatment, + title = "Treatment Side Effect Prediction from Online User-generated Content", + author = "Nguyen, Van Hoang and + Sugiyama, Kazunari and + Kan, Min-Yen and + Halder, Kishaloy", + editor = "Lavelli, Alberto and + Minard, Anne-Lyse and + Rinaldi, Fabio", + booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis", + month = oct, + year = "2018", + address = "Brussels, Belgium", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W18-5602", + doi = "10.18653/v1/W18-5602", + pages = "12--21", + abstract = "With Health 2.0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities. These are helpful platforms for non-professional medical opinions, yet pose risk of being unreliable in quality and insufficient in quantity to cover the wide range of potential drug reactions. Existing approaches which analyze such user-generated content in online forums heavily rely on feature engineering of both documents and users, and often overlook the relationships between posts within a common discussion thread. Inspired by recent advancements, we propose a neural architecture that models the textual content of user-generated documents and user experiences in online communities to predict side effects during treatment. Experimental results show that our proposed architecture outperforms baseline models.", +} + + +@inproceedings{chandrasekaran-kan-2018-countering, + title = "Countering Position Bias in Instructor Interventions in {MOOC} Discussion Forums", + author = "Chandrasekaran, Muthu Kumar and + Kan, Min-Yen", + editor = "Tseng, Yuen-Hsien and + Chen, Hsin-Hsi and + Ng, Vincent and + Komachi, Mamoru", + booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications", + month = jul, + year = "2018", + address = "Melbourne, Australia", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W18-3720", + doi = "10.18653/v1/W18-3720", + pages = "135--142", + abstract = "We systematically confirm that instructors are strongly influenced by the user interface presentation of Massive Online Open Course (MOOC) discussion forums. In a large scale dataset, we conclusively show that instructor interventions exhibit strong position bias, as measured by the position where the thread appeared on the user interface at the time of intervention. We measure and remove this bias, enabling unbiased statistical modelling and evaluation. We show that our de-biased classifier improves predicting interventions over the state-of-the-art on courses with sufficient number of interventions by 8.2{\%} in F1 and 24.4{\%} in recall on average.", +} + + +@inproceedings{gildea-etal-2018-acl, + title = "The {ACL} {A}nthology: Current State and Future Directions", + author = "Gildea, Daniel and + Kan, Min-Yen and + Madnani, Nitin and + Teichmann, Christoph and + Villalba, Mart{\'\i}n", + editor = "Park, Eunjeong L. and + Hagiwara, Masato and + Milajevs, Dmitrijs and + Tan, Liling", + booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})", + month = jul, + year = "2018", + address = "Melbourne, Australia", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W18-2504", + doi = "10.18653/v1/W18-2504", + pages = "23--28", + abstract = "The Association of Computational Linguistic{'}s Anthology is the open source archive, and the main source for computational linguistics and natural language processing{'}s scientific literature. The ACL Anthology is currently maintained exclusively by community volunteers and has to be available and up-to-date at all times. We first discuss the current, open source approach used to achieve this, and then discuss how the planned use of Docker images will improve the Anthology{'}s long-term stability. This change will make it easier for researchers to utilize Anthology data for experimentation. We believe the ACL community can directly benefit from the extension-friendly architecture of the Anthology. We end by issuing an open challenge of reviewer matching we encourage the community to rally towards.", +} + + +@inproceedings{lei-etal-2018-sequicity, + title = "{S}equicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures", + author = "Lei, Wenqiang and + Jin, Xisen and + Kan, Min-Yen and + Ren, Zhaochun and + He, Xiangnan and + Yin, Dawei", + editor = "Gurevych, Iryna and + Miyao, Yusuke", + booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jul, + year = "2018", + address = "Melbourne, Australia", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P18-1133", + doi = "10.18653/v1/P18-1133", + pages = "1437--1447", + abstract = "Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.", +} + + +@inproceedings{jiang-etal-2018-identifying, + title = "Identifying Emergent Research Trends by Key Authors and Phrases", + author = "Jiang, Shenhao and + Prasad, Animesh and + Kan, Min-Yen and + Sugiyama, Kazunari", + editor = "Bender, Emily M. and + Derczynski, Leon and + Isabelle, Pierre", + booktitle = "Proceedings of the 27th International Conference on Computational Linguistics", + month = aug, + year = "2018", + address = "Santa Fe, New Mexico, USA", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/C18-1022", + pages = "259--269", + abstract = "Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield insight on developing topics. We propose an embedded trend detection framework for this task which incorporates our bijunctive hypothesis that important phrases are written by important authors within a field and vice versa. By ranking both author and phrase information in a multigraph, our method jointly determines key phrases and authoritative authors. We represent this intermediate output as phrasal embeddings, and feed this to a recurrent neural network (RNN) to compute trend scores that identify research trends. Over two large datasets of scientific articles, we demonstrate that our approach successfully detects past trends from the field, outperforming baselines based solely on text centrality or citation.", +} + + +@inproceedings{halder-etal-2017-modeling, + title = "Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach", + author = "Halder, Kishaloy and + Poddar, Lahari and + Kan, Min-Yen", + editor = "Balahur, Alexandra and + Mohammad, Saif M. and + van der Goot, Erik", + booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis", + month = sep, + year = "2017", + address = "Copenhagen, Denmark", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W17-5217", + doi = "10.18653/v1/W17-5217", + pages = "127--135", + abstract = "Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual{'}s posts. We analyze a real-world dataset from the Mental Health section of HealthBoards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely. We study the problem of predicting a patient{'}s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.", +} + + +@inproceedings{prasad-kan-2017-wing, + title = "{WING}-{NUS} at {S}em{E}val-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling", + author = "Prasad, Animesh and + Kan, Min-Yen", + editor = "Bethard, Steven and + Carpuat, Marine and + Apidianaki, Marianna and + Mohammad, Saif M. and + Cer, Daniel and + Jurgens, David", + booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)", + month = aug, + year = "2017", + address = "Vancouver, Canada", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/S17-2170", + doi = "10.18653/v1/S17-2170", + pages = "973--977", + abstract = "We describe an end-to-end pipeline processing approach for SemEval 2017{'}s Task 10 to extract keyphrases and their relations from scientific publications. We jointly identify and classify keyphrases by modeling the subtasks as sequential labeling. Our system utilizes standard, surface-level features along with the adjacent word features, and performs conditional decoding on whole text to extract keyphrases. We focus only on the identification and typing of keyphrases (Subtasks A and B, together referred as extraction), but provide an end-to-end system inclusive of keyphrase relation identification (Subtask C) for completeness. Our top performing configuration achieves an $F_1$ of 0.27 for the end-to-end keyphrase extraction and relation identification scenario on the final test data, and compares on par to other top ranked systems for keyphrase extraction. Our system outperforms other techniques that do not employ global decoding and hence do not account for dependencies between keyphrases. We believe this is crucial for keyphrase classification in the given context of scientific document mining.", +} + + +@proceedings{acl-2017-association-linguistics, + title = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", + editor = "Barzilay, Regina and + Kan, Min-Yen", + month = jul, + year = "2017", + address = "Vancouver, Canada", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P17-2000", + doi = "10.18653/v1/P17-2", +} + + +@proceedings{ws-2016-joint, + title = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})", + editor = "Cabanac, Guillaume and + Chandrasekaran, Muthu Kumar and + Frommholz, Ingo and + Jaidka, Kokil and + Kan, Min-Yen and + Mayr, Philipp and + Wolfram, Dietmar", + month = jun, + year = "2016", + url = "https://aclanthology.org/W16-1500", + pages = "1--5", +} + + +@inproceedings{chen-etal-2015-interactive, + title = "Interactive Second Language Learning from News Websites", + author = "Chen, Tao and + Zheng, Naijia and + Zhao, Yue and + Chandrasekaran, Muthu Kumar and + Kan, Min-Yen", + editor = "Chen, Hsin-Hsi and + Tseng, Yuen-Hsien and + Matsumoto, Yuji and + Wong, Lung Hsiang", + booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications", + month = jul, + year = "2015", + address = "Beijing, China", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W15-4406", + doi = "10.18653/v1/W15-4406", + pages = "34--42", +} + + +@inproceedings{kan-2015-keywords, + title = "Keywords, phrases, clauses and sentences: topicality, indicativeness and informativeness at scales", + author = "Kan, Min-Yen", + editor = "Gollapalli, Sujatha Das and + Caragea, Cornelia and + Li, Xiaoli and + Giles, C. Lee", + booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction", + month = jul, + year = "2015", + address = "Beijing, China", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W15-3601", + doi = "10.18653/v1/W15-3601", + pages = "1", +} + + +@inproceedings{ng-etal-2014-exploiting, + title = "Exploiting Timelines to Enhance Multi-document Summarization", + author = "Ng, Jun-Ping and + Chen, Yan and + Kan, Min-Yen and + Li, Zhoujun", + editor = "Toutanova, Kristina and + Wu, Hua", + booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jun, + year = "2014", + address = "Baltimore, Maryland", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P14-1087", + doi = "10.3115/v1/P14-1087", + pages = "923--933", +} + + +@inproceedings{wang-kan-2013-mining, + title = "Mining Informal Language from {C}hinese Microtext: Joint Word Recognition and Segmentation", + author = "Wang, Aobo and + Kan, Min-Yen", + editor = "Schuetze, Hinrich and + Fung, Pascale and + Poesio, Massimo", + booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = aug, + year = "2013", + address = "Sofia, Bulgaria", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P13-1072", + pages = "731--741", +} + + +@inproceedings{wang-etal-2013-chinese, + title = "{C}hinese Informal Word Normalization: an Experimental Study", + author = "Wang, Aobo and + Kan, Min-Yen and + Andrade, Daniel and + Onishi, Takashi and + Ishikawa, Kai", + editor = "Mitkov, Ruslan and + Park, Jong C.", + booktitle = "Proceedings of the Sixth International Joint Conference on Natural Language Processing", + month = oct, + year = "2013", + address = "Nagoya, Japan", + publisher = "Asian Federation of Natural Language Processing", + url = "https://aclanthology.org/I13-1015", + pages = "127--135", +} + + +@inproceedings{ng-etal-2013-exploiting, + title = "Exploiting Discourse Analysis for Article-Wide Temporal Classification", + author = "Ng, Jun-Ping and + Kan, Min-Yen and + Lin, Ziheng and + Feng, Wei and + Chen, Bin and + Su, Jian and + Tan, Chew-Lim", + editor = "Yarowsky, David and + Baldwin, Timothy and + Korhonen, Anna and + Livescu, Karen and + Bethard, Steven", + booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", + month = oct, + year = "2013", + address = "Seattle, Washington, USA", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/D13-1002", + pages = "12--23", +} + + +@inproceedings{jin-etal-2013-mining, + title = "Mining Scientific Terms and their Definitions: A Study of the {ACL} {A}nthology", + author = "Jin, Yiping and + Kan, Min-Yen and + Ng, Jun-Ping and + He, Xiangnan", + editor = "Yarowsky, David and + Baldwin, Timothy and + Korhonen, Anna and + Livescu, Karen and + Bethard, Steven", + booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", + month = oct, + year = "2013", + address = "Seattle, Washington, USA", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/D13-1073", + pages = "780--790", +} + + +@inproceedings{bysani-kan-2012-integrating, + title = "Integrating User-Generated Content in the {ACL} {A}nthology", + author = "Bysani, Praveen and + Kan, Min-Yen", + editor = "Banchs, Rafael E.", + booktitle = "Proceedings of the {ACL}-2012 Special Workshop on Rediscovering 50 Years of Discoveries", + month = jul, + year = "2012", + address = "Jeju Island, Korea", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W12-3209", + pages = "83--87", +} + + +@inproceedings{wang-etal-2012-tweeting, + title = "Re-tweeting from a linguistic perspective", + author = "Wang, Aobo and + Chen, Tao and + Kan, Min-Yen", + editor = "Sood, Sara Owsley and + Nagarajan, Meenakshi and + Gamon, Michael", + booktitle = "Proceedings of the Second Workshop on Language in Social Media", + month = jun, + year = "2012", + address = "Montr{\'e}al, Canada", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W12-2106", + pages = "46--55", +} + + +@inproceedings{lin-etal-2012-combining, + title = "Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization Evaluation", + author = "Lin, Ziheng and + Liu, Chang and + Ng, Hwee Tou and + Kan, Min-Yen", + editor = "Li, Haizhou and + Lin, Chin-Yew and + Osborne, Miles and + Lee, Gary Geunbae and + Park, Jong C.", + booktitle = "Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jul, + year = "2012", + address = "Jeju Island, Korea", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P12-1106", + pages = "1006--1014", +} + + +@inproceedings{ng-etal-2012-exploiting, + title = "Exploiting Category-Specific Information for Multi-Document Summarization", + author = "Ng, Jun-Ping and + Bysani, Praveen and + Lin, Ziheng and + Kan, Min-Yen and + Tan, Chew-Lim", + editor = "Kay, Martin and + Boitet, Christian", + booktitle = "Proceedings of {COLING} 2012", + month = dec, + year = "2012", + address = "Mumbai, India", + publisher = "The COLING 2012 Organizing Committee", + url = "https://aclanthology.org/C12-1128", + pages = "2093--2108", +} + + +@inproceedings{ng-kan-2012-improved, + title = "Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations", + author = "Ng, Jun-Ping and + Kan, Min-Yen", + editor = "Kay, Martin and + Boitet, Christian", + booktitle = "Proceedings of {COLING} 2012", + month = dec, + year = "2012", + address = "Mumbai, India", + publisher = "The COLING 2012 Organizing Committee", + url = "https://aclanthology.org/C12-1129", + pages = "2109--2124", +} + + +@inproceedings{lin-etal-2011-automatically, + title = "Automatically Evaluating Text Coherence Using Discourse Relations", + author = "Lin, Ziheng and + Ng, Hwee Tou and + Kan, Min-Yen", + editor = "Lin, Dekang and + Matsumoto, Yuji and + Mihalcea, Rada", + booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies", + month = jun, + year = "2011", + address = "Portland, Oregon, USA", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P11-1100", + pages = "997--1006", +} + + +@inproceedings{lin-etal-2010-extracting, + title = "Extracting Formulaic and Free Text Clinical Research Articles Metadata using Conditional Random Fields", + author = "Lin, Sein and + Ng, Jun-Ping and + Pradhan, Shreyasee and + Shah, Jatin and + Pietrobon, Ricardo and + Kan, Min-Yen", + editor = "Dalianis, Hercules and + Hassel, Martin and + Nilsson, Gunnar", + booktitle = "Proceedings of the {NAACL} {HLT} 2010 Second Louhi Workshop on Text and Data Mining of Health Documents", + month = jun, + year = "2010", + address = "Los Angeles, California, USA", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W10-1114", + pages = "90--95", +} + + +@inproceedings{kim-etal-2010-semeval, + title = "{S}em{E}val-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles", + author = "Kim, Su Nam and + Medelyan, Olena and + Kan, Min-Yen and + Baldwin, Timothy", + editor = "Erk, Katrin and + Strapparava, Carlo", + booktitle = "Proceedings of the 5th International Workshop on Semantic Evaluation", + month = jul, + year = "2010", + address = "Uppsala, Sweden", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/S10-1004", + pages = "21--26", +} + + +@inproceedings{luong-etal-2010-hybrid, + title = "A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages", + author = "Luong, Minh-Thang and + Nakov, Preslav and + Kan, Min-Yen", + editor = "Li, Hang and + M{\`a}rquez, Llu{\'\i}s", + booktitle = "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing", + month = oct, + year = "2010", + address = "Cambridge, MA", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/D10-1015", + pages = "148--157", +} + + +@inproceedings{hoang-kan-2010-towards, + title = "Towards Automated Related Work Summarization", + author = "Hoang, Cong Duy Vu and + Kan, Min-Yen", + editor = "Huang, Chu-Ren and + Jurafsky, Dan", + booktitle = "Coling 2010: Posters", + month = aug, + year = "2010", + address = "Beijing, China", + publisher = "Coling 2010 Organizing Committee", + url = "https://aclanthology.org/C10-2049", + pages = "427--435", +} + + +@inproceedings{kim-etal-2010-evaluating, + title = "Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extraction", + author = "Kim, Su Nam and + Baldwin, Timothy and + Kan, Min-Yen", + editor = "Huang, Chu-Ren and + Jurafsky, Dan", + booktitle = "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)", + month = aug, + year = "2010", + address = "Beijing, China", + publisher = "Coling 2010 Organizing Committee", + url = "https://aclanthology.org/C10-1065", + pages = "572--580", +} + + +@inproceedings{luong-kan-2010-enhancing, + title = "Enhancing Morphological Alignment for Translating Highly Inflected Languages", + author = "Luong, Minh-Thang and + Kan, Min-Yen", + editor = "Huang, Chu-Ren and + Jurafsky, Dan", + booktitle = "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)", + month = aug, + year = "2010", + address = "Beijing, China", + publisher = "Coling 2010 Organizing Committee", + url = "https://aclanthology.org/C10-1084", + pages = "743--751", +} + + +@proceedings{ws-2009-2009-text, + title = "Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries ({NLPIR}4{DL})", + editor = "Kan, Min-Yen and + Teufel, Simone", + month = aug, + year = "2009", + address = "Suntec City, Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W09-3600", +} + + +@inproceedings{kim-kan-2009-examining, + title = "Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles", + author = "Kim, Su Nam and + Kan, Min-Yen", + editor = "Anastasiou, Dimitra and + Hashimoto, Chikara and + Nakov, Preslav and + Kim, Su Nam", + booktitle = "Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications ({MWE} 2009)", + month = aug, + year = "2009", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W09-2902", + pages = "9--16", +} + + +@inproceedings{hoang-etal-2009-examination, + title = "A re-examination of lexical association measures", + author = "Hoang, Hung Huu and + Kim, Su Nam and + Kan, Min-Yen", + editor = "Anastasiou, Dimitra and + Hashimoto, Chikara and + Nakov, Preslav and + Kim, Su Nam", + booktitle = "Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications ({MWE} 2009)", + month = aug, + year = "2009", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W09-2905", + pages = "31--39", +} + + +@inproceedings{kim-etal-2009-extracting, + title = "Extracting Domain-Specific Words - A Statistical Approach", + author = "Kim, Su Nam and + Baldwin, Timothy and + Kan, Min-Yen", + editor = "Pizzato, Luiz Augusto and + Schwitter, Rolf", + booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2009", + month = dec, + year = "2009", + address = "Sydney, Australia", + url = "https://aclanthology.org/U09-1013", + pages = "94--98", +} + + +@inproceedings{setiawan-etal-2009-topological, + title = "Topological Ordering of Function Words in Hierarchical Phrase-based Translation", + author = "Setiawan, Hendra and + Kan, Min-Yen and + Li, Haizhou and + Resnik, Philip", + editor = "Su, Keh-Yih and + Su, Jian and + Wiebe, Janyce and + Li, Haizhou", + booktitle = "Proceedings of the Joint Conference of the 47th Annual Meeting of the {ACL} and the 4th International Joint Conference on Natural Language Processing of the {AFNLP}", + month = aug, + year = "2009", + address = "Suntec, Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P09-1037", + pages = "324--332", +} + + +@inproceedings{lin-etal-2009-recognizing, + title = "Recognizing Implicit Discourse Relations in the {P}enn {D}iscourse {T}reebank", + author = "Lin, Ziheng and + Kan, Min-Yen and + Ng, Hwee Tou", + editor = "Koehn, Philipp and + Mihalcea, Rada", + booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing", + month = aug, + year = "2009", + address = "Singapore", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/D09-1036", + pages = "343--351", +} + + +@inproceedings{bird-etal-2008-acl, + title = "The {ACL} {A}nthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics", + author = "Bird, Steven and + Dale, Robert and + Dorr, Bonnie and + Gibson, Bryan and + Joseph, Mark and + Kan, Min-Yen and + Lee, Dongwon and + Powley, Brett and + Radev, Dragomir and + Tan, Yee Fan", + editor = "Calzolari, Nicoletta and + Choukri, Khalid and + Maegaard, Bente and + Mariani, Joseph and + Odijk, Jan and + Piperidis, Stelios and + Tapias, Daniel", + booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)", + month = may, + year = "2008", + address = "Marrakech, Morocco", + publisher = "European Language Resources Association (ELRA)", + url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/445_paper.pdf", + abstract = "The ACL Anthology is a digital archive of conference and journal papers in natural language processing and computational linguistics. Its primary purpose is to serve as a reference repository of research results, but we believe that it can also be an object of study and a platform for research in its own right. We describe an enriched and standardized reference corpus derived from the ACL Anthology that can be used for research in scholarly document processing. This corpus, which we call the ACL Anthology Reference Corpus (ACL ARC), brings together the recent activities of a number of research groups around the world. Our goal is to make the corpus widely available, and to encourage other researchers to use it as a standard testbed for experiments in both bibliographic and bibliometric research.", +} + + +@inproceedings{councill-etal-2008-parscit, + title = "{P}ars{C}it: an Open-source {CRF} Reference String Parsing Package", + author = "Councill, Isaac and + Giles, C. Lee and + Kan, Min-Yen", + editor = "Calzolari, Nicoletta and + Choukri, Khalid and + Maegaard, Bente and + Mariani, Joseph and + Odijk, Jan and + Piperidis, Stelios and + Tapias, Daniel", + booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)", + month = may, + year = "2008", + address = "Marrakech, Morocco", + publisher = "European Language Resources Association (ELRA)", + url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/166_paper.pdf", + abstract = "We describe ParsCit, a freely available, open-source implementation of a reference string parsing package. At the core of ParsCit is a trained conditional random field (CRF) model used to label the token sequences in the reference string. A heuristic model wraps this core with added functionality to identify reference strings from a plain text file, and to retrieve the citation contexts. The package comes with utilities to run it as a web service or as a standalone utility. We compare ParsCit on three distinct reference string datasets and show that it compares well with other previously published work.", +} + + +@inproceedings{qiu-etal-2008-modeling, + title = "Modeling Context in Scenario Template Creation", + author = "Qiu, Long and + Kan, Min-Yen and + Chua, Tat-Seng", + booktitle = "Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-{I}", + year = "2008", + url = "https://aclanthology.org/I08-1021", +} + + +@inproceedings{lin-kan-2007-timestamped, + title = "Timestamped Graphs: Evolutionary Models of Text for Multi-Document Summarization", + author = "Lin, Ziheng and + Kan, Min-Yen", + editor = "Biemann, Chris and + Matveeva, Irina and + Mihalcea, Rada and + Radev, Dragomir", + booktitle = "Proceedings of the Second Workshop on {T}ext{G}raphs: Graph-Based Algorithms for Natural Language Processing", + year = "2007", + address = "Rochester, NY, USA", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W07-0204", + pages = "25--32", +} + + +@inproceedings{elmacioglu-etal-2007-psnus, + title = "{PSNUS}: Web People Name Disambiguation by Simple Clustering with Rich Features", + author = "Elmacioglu, Ergin and + Tan, Yee Fan and + Yan, Su and + Kan, Min-Yen and + Lee, Dongwon", + editor = "Agirre, Eneko and + M{\`a}rquez, Llu{\'\i}s and + Wicentowski, Richard", + booktitle = "Proceedings of the Fourth International Workshop on Semantic Evaluations ({S}em{E}val-2007)", + month = jun, + year = "2007", + address = "Prague, Czech Republic", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/S07-1058", + pages = "268--271", +} + + +@inproceedings{setiawan-etal-2007-ordering, + title = "Ordering Phrases with Function Words", + author = "Setiawan, Hendra and + Kan, Min-Yen and + Li, Haizhou", + editor = "Zaenen, Annie and + van den Bosch, Antal", + booktitle = "Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics", + month = jun, + year = "2007", + address = "Prague, Czech Republic", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P07-1090", + pages = "712--719", +} + + +@inproceedings{tan-etal-2006-extending, + title = "Extending corpus-based identification of light verb constructions using a supervised learning framework", + author = "Tan, Yee Fan and + Kan, Min-Yen and + Cui, Hang", + booktitle = "Proceedings of the Workshop on Multi-word-expressions in a multilingual context", + year = "2006", + url = "https://aclanthology.org/W06-2407", +} + + +@inproceedings{qiu-etal-2006-paraphrase, + title = "Paraphrase Recognition via Dissimilarity Significance Classification", + author = "Qiu, Long and + Kan, Min-Yen and + Chua, Tat-Seng", + editor = "Jurafsky, Dan and + Gaussier, Eric", + booktitle = "Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing", + month = jul, + year = "2006", + address = "Sydney, Australia", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W06-1603", + pages = "18--26", +} + + +@inproceedings{qiu-etal-2004-public, + title = "A Public Reference Implementation of the {RAP} Anaphora Resolution Algorithm", + author = "Qiu, Long and + Kan, Min-Yen and + Chua, Tat-Seng", + editor = "Lino, Maria Teresa and + Xavier, Maria Francisca and + Ferreira, F{\'a}tima and + Costa, Rute and + Silva, Raquel", + booktitle = "Proceedings of the Fourth International Conference on Language Resources and Evaluation ({LREC}{'}04)", + month = may, + year = "2004", + address = "Lisbon, Portugal", + publisher = "European Language Resources Association (ELRA)", + url = "http://www.lrec-conf.org/proceedings/lrec2004/pdf/778.pdf", +} + + +@inproceedings{kan-mckeown-2002-corpus, + title = "Corpus-trained Text Generation for Summarization", + author = "Kan, Min-Yen and + McKeown, Kathleen R.", + editor = "McKeown, Kathleen", + booktitle = "Proceedings of the International Natural Language Generation Conference", + month = jul, + year = "2002", + address = "Harriman, New York, USA", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W02-2101", + pages = "1--8", +} + + +@inproceedings{kan-etal-2002-using, + title = "Using the Annotated Bibliography as a Resource for Indicative Summarization", + author = "Kan, Min-Yen and + Klavans, Judith L. and + McKeown, Kathleen R.", + editor = "Gonz{\'a}lez Rodr{\'\i}guez, Manuel and + Suarez Araujo, Carmen Paz", + booktitle = "Proceedings of the Third International Conference on Language Resources and Evaluation ({LREC}{'}02)", + month = may, + year = "2002", + address = "Las Palmas, Canary Islands - Spain", + publisher = "European Language Resources Association (ELRA)", + url = "http://www.lrec-conf.org/proceedings/lrec2002/pdf/7.pdf", +} + + +@inproceedings{kan-etal-2001-applying, + title = "Applying Natural Language Generation to Indicative Summarization", + author = "Kan, Min-Yen and + McKeown, Kathleen R. and + Klavans, Judith L.", + editor = "Horacek, Helmut and + Nicolov, Nicolas and + Wanner, Leo", + booktitle = "Proceedings of the {ACL} 2001 Eighth {E}uropean Workshop on Natural Language Generation ({EWNLG})", + year = "2001", + address = "Toulouse, France", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/W01-0813", +} + + +@inproceedings{kan-etal-1998-linear, + title = "Linear Segmentation and Segment Significance", + author = "Kan, Min-Yen and + Klavans, Judith L. and + McKeown, Kathleen R.", + booktitle = "Sixth Workshop on Very Large Corpora", + year = "1998", + url = "https://aclanthology.org/W98-1123", +} + + +@inproceedings{klavans-kan-1998-role-verbs, + title = "Role of Verbs in Document Analysis", + author = "Klavans, Judith L. and + Kan, Min-Yen", + booktitle = "36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1", + month = aug, + year = "1998", + address = "Montreal, Quebec, Canada", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/P98-1112", + doi = "10.3115/980845.980959", + pages = "680--686", +} + From 62721cca48e4ee5dbb8939fa22fd3aeca44b963f Mon Sep 17 00:00:00 2001 From: knmnyn <217067+knmnyn@users.noreply.github.com> Date: Thu, 11 Jul 2024 07:40:57 +0000 Subject: [PATCH 12/22] content: import publications from Bibtex --- .../acl-2017-association-linguistics/cite.bib | 11 ++++++ .../acl-2017-association-linguistics/index.md | 16 ++++++++ .../bhola-etal-2020-retrieving/cite.bib | 19 ++++++++++ .../bhola-etal-2020-retrieving/index.md | 37 +++++++++++++++++++ .../publication/bird-etal-2008-acl/cite.bib | 27 ++++++++++++++ .../publication/bird-etal-2008-acl/index.md | 32 ++++++++++++++++ .../bysani-kan-2012-integrating/cite.bib | 13 +++++++ .../bysani-kan-2012-integrating/index.md | 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content/publication/ws-2016-joint/index.md create mode 100644 content/publication/xie-etal-2020-exploring/cite.bib create mode 100644 content/publication/xie-etal-2020-exploring/index.md diff --git a/content/publication/acl-2017-association-linguistics/cite.bib b/content/publication/acl-2017-association-linguistics/cite.bib new file mode 100644 index 0000000..c47d1af --- /dev/null +++ b/content/publication/acl-2017-association-linguistics/cite.bib @@ -0,0 +1,11 @@ +@proceedings{acl-2017-association-linguistics, + address = {Vancouver, Canada}, + doi = {10.18653/v1/P17-2}, + editor = {Barzilay, Regina and +Kan, Min-Yen}, + month = {July}, + publisher = {Association for Computational Linguistics}, + title = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, + url = {https://aclanthology.org/P17-2000}, + year = {2017} +} diff --git a/content/publication/acl-2017-association-linguistics/index.md b/content/publication/acl-2017-association-linguistics/index.md new file mode 100644 index 0000000..b03138f --- /dev/null +++ b/content/publication/acl-2017-association-linguistics/index.md @@ -0,0 +1,16 @@ +--- +title: 'Proceedings of the 55th Annual Meeting of the Association for Computational + Linguistics (Volume 2: Short Papers)' +authors: +- Regina Barzilay +- Min-Yen Kan +date: '2017-07-01' +publishDate: '2024-07-11T07:40:56.389063Z' +publication_types: +- book +publication: '*Association for Computational Linguistics*' +doi: 10.18653/v1/P17-2 +links: +- name: URL + url: https://aclanthology.org/P17-2000 +--- diff --git a/content/publication/bhola-etal-2020-retrieving/cite.bib b/content/publication/bhola-etal-2020-retrieving/cite.bib new file mode 100644 index 0000000..d75806b --- /dev/null +++ b/content/publication/bhola-etal-2020-retrieving/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{bhola-etal-2020-retrieving, + abstract = {We introduce a deep learning model to learn the set of enumerated job skills associated with a job description. In our analysis of a large-scale government job portal mycareersfuture.sg, we observe that as much as 65% of job descriptions miss describing a significant number of relevant skills. Our model addresses this task from the perspective of an extreme multi-label classification (XMLC) problem, where descriptions are the evidence for the binary relevance of thousands of individual skills. Building upon the current state-of-the-art language modeling approaches such as BERT, we show our XMLC method improves on an existing baseline solution by over 9% and 7% absolute improvements in terms of recall and normalized discounted cumulative gain. We further show that our approach effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings by taking into account the structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process. We further show that our approach, to ensure the BERT-XMLC model accounts for structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process, effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings. To facilitate future research and replication of our work, we have made the dataset and the implementation of our model publicly available.}, + address = {Barcelona, Spain (Online)}, + author = {Bhola, Akshay and +Halder, Kishaloy and +Prasad, Animesh and +Kan, Min-Yen}, + booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, + doi = {10.18653/v1/2020.coling-main.513}, + editor = {Scott, Donia and +Bel, Nuria and +Zong, Chengqing}, + month = {December}, + pages = {5832--5842}, + publisher = {International Committee on Computational Linguistics}, + title = {Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework}, + url = {https://aclanthology.org/2020.coling-main.513}, + year = {2020} +} diff --git a/content/publication/bhola-etal-2020-retrieving/index.md b/content/publication/bhola-etal-2020-retrieving/index.md new file mode 100644 index 0000000..7795f4c --- /dev/null +++ b/content/publication/bhola-etal-2020-retrieving/index.md @@ -0,0 +1,37 @@ +--- +title: 'Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label + Classification Framework' +authors: +- Akshay Bhola +- Kishaloy Halder +- Animesh Prasad +- Min-Yen Kan +date: '2020-12-01' +publishDate: '2024-07-11T07:40:56.291153Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 28th International Conference on Computational Linguistics*' +doi: 10.18653/v1/2020.coling-main.513 +abstract: We introduce a deep learning model to learn the set of enumerated job skills + associated with a job description. In our analysis of a large-scale government job + portal mycareersfuture.sg, we observe that as much as 65% of job descriptions miss + describing a significant number of relevant skills. Our model addresses this task + from the perspective of an extreme multi-label classification (XMLC) problem, where + descriptions are the evidence for the binary relevance of thousands of individual + skills. Building upon the current state-of-the-art language modeling approaches + such as BERT, we show our XMLC method improves on an existing baseline solution + by over 9% and 7% absolute improvements in terms of recall and normalized discounted + cumulative gain. We further show that our approach effectively addresses the missing + skills problem, and helps in recovering relevant skills that were missed out in + the job postings by taking into account the structured semantic representation of + skills and their co-occurrences through a Correlation Aware Bootstrapping process. + We further show that our approach, to ensure the BERT-XMLC model accounts for structured + semantic representation of skills and their co-occurrences through a Correlation + Aware Bootstrapping process, effectively addresses the missing skills problem, and + helps in recovering relevant skills that were missed out in the job postings. To + facilitate future research and replication of our work, we have made the dataset + and the implementation of our model publicly available. +links: +- name: URL + url: https://aclanthology.org/2020.coling-main.513 +--- diff --git a/content/publication/bird-etal-2008-acl/cite.bib b/content/publication/bird-etal-2008-acl/cite.bib new file mode 100644 index 0000000..77787d9 --- /dev/null +++ b/content/publication/bird-etal-2008-acl/cite.bib @@ -0,0 +1,27 @@ +@inproceedings{bird-etal-2008-acl, + abstract = {The ACL Anthology is a digital archive of conference and journal papers in natural language processing and computational linguistics. Its primary purpose is to serve as a reference repository of research results, but we believe that it can also be an object of study and a platform for research in its own right. We describe an enriched and standardized reference corpus derived from the ACL Anthology that can be used for research in scholarly document processing. This corpus, which we call the ACL Anthology Reference Corpus (ACL ARC), brings together the recent activities of a number of research groups around the world. Our goal is to make the corpus widely available, and to encourage other researchers to use it as a standard testbed for experiments in both bibliographic and bibliometric research.}, + address = {Marrakech, Morocco}, + author = {Bird, Steven and +Dale, Robert and +Dorr, Bonnie and +Gibson, Bryan and +Joseph, Mark and +Kan, Min-Yen and +Lee, Dongwon and +Powley, Brett and +Radev, Dragomir and +Tan, Yee Fan}, + booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, + editor = {Calzolari, Nicoletta and +Choukri, Khalid and +Maegaard, Bente and +Mariani, Joseph and +Odijk, Jan and +Piperidis, Stelios and +Tapias, Daniel}, + month = {May}, + publisher = {European Language Resources Association (ELRA)}, + title = {The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics}, + url = {http://www.lrec-conf.org/proceedings/lrec2008/pdf/445_paper.pdf}, + year = {2008} +} diff --git a/content/publication/bird-etal-2008-acl/index.md b/content/publication/bird-etal-2008-acl/index.md new file mode 100644 index 0000000..adc4baa --- /dev/null +++ b/content/publication/bird-etal-2008-acl/index.md @@ -0,0 +1,32 @@ +--- +title: 'The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic + Research in Computational Linguistics' +authors: +- Steven Bird +- Robert Dale +- Bonnie Dorr +- Bryan Gibson +- Mark Joseph +- Min-Yen Kan +- Dongwon Lee +- Brett Powley +- Dragomir Radev +- Yee Fan Tan +date: '2008-05-01' +publishDate: '2024-07-11T07:40:56.554775Z' +publication_types: +- paper-conference +publication: "*Proceedings of the Sixth International Conference on Language Resources + and Evaluation (LREC'08)*" +abstract: The ACL Anthology is a digital archive of conference and journal papers + in natural language processing and computational linguistics. Its primary purpose + is to serve as a reference repository of research results, but we believe that it + can also be an object of study and a platform for research in its own right. We + describe an enriched and standardized reference corpus derived from the ACL Anthology + that can be used for research in scholarly document processing. This corpus, which + we call the ACL Anthology Reference Corpus (ACL ARC), brings together the recent + activities of a number of research groups around the world. Our goal is to make + the corpus widely available, and to encourage other researchers to use it as a standard + testbed for experiments in both bibliographic and bibliometric research. +url_pdf: http://www.lrec-conf.org/proceedings/lrec2008/pdf/445_paper.pdf +--- diff --git a/content/publication/bysani-kan-2012-integrating/cite.bib b/content/publication/bysani-kan-2012-integrating/cite.bib new file mode 100644 index 0000000..b20d1a8 --- /dev/null +++ b/content/publication/bysani-kan-2012-integrating/cite.bib @@ -0,0 +1,13 @@ +@inproceedings{bysani-kan-2012-integrating, + address = {Jeju Island, Korea}, + author = {Bysani, Praveen and +Kan, Min-Yen}, + booktitle = {Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries}, + editor = {Banchs, Rafael E.}, + month = {July}, + pages = {83--87}, + publisher = {Association for Computational Linguistics}, + title = {Integrating User-Generated Content in the ACL Anthology}, + url = {https://aclanthology.org/W12-3209}, + year = {2012} +} diff --git a/content/publication/bysani-kan-2012-integrating/index.md b/content/publication/bysani-kan-2012-integrating/index.md new file mode 100644 index 0000000..36ad406 --- /dev/null +++ b/content/publication/bysani-kan-2012-integrating/index.md @@ -0,0 +1,15 @@ +--- +title: Integrating User-Generated Content in the ACL Anthology +authors: +- Praveen Bysani +- Min-Yen Kan +date: '2012-07-01' +publishDate: '2024-07-11T07:40:56.444716Z' +publication_types: +- paper-conference +publication: '*Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years + of Discoveries*' +links: +- name: URL + url: https://aclanthology.org/W12-3209 +--- diff --git a/content/publication/cao-etal-2020-expertise/cite.bib b/content/publication/cao-etal-2020-expertise/cite.bib new file mode 100644 index 0000000..5f265cc --- /dev/null +++ b/content/publication/cao-etal-2020-expertise/cite.bib @@ -0,0 +1,22 @@ +@inproceedings{cao-etal-2020-expertise, + abstract = {The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at r̆lhttps://srhthu.github.io/expertise-style-transfer/.}, + address = {Online}, + author = {Cao, Yixin and +Shui, Ruihao and +Pan, Liangming and +Kan, Min-Yen and +Liu, Zhiyuan and +Chua, Tat-Seng}, + booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, + doi = {10.18653/v1/2020.acl-main.100}, + editor = {Jurafsky, Dan and +Chai, Joyce and +Schluter, Natalie and +Tetreault, Joel}, + month = {July}, + pages = {1061--1071}, + publisher = {Association for Computational Linguistics}, + title = {Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen}, + url = {https://aclanthology.org/2020.acl-main.100}, + year = {2020} +} diff --git a/content/publication/cao-etal-2020-expertise/index.md b/content/publication/cao-etal-2020-expertise/index.md new file mode 100644 index 0000000..184bc62 --- /dev/null +++ b/content/publication/cao-etal-2020-expertise/index.md @@ -0,0 +1,33 @@ +--- +title: 'Expertise Style Transfer: A New Task Towards Better Communication between + Experts and Laymen' +authors: +- Yixin Cao +- Ruihao Shui +- Liangming Pan +- Min-Yen Kan +- Zhiyuan Liu +- Tat-Seng Chua +date: '2020-07-01' +publishDate: '2024-07-11T07:40:56.298355Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 58th Annual Meeting of the Association for Computational + Linguistics*' +doi: 10.18653/v1/2020.acl-main.100 +abstract: The curse of knowledge can impede communication between experts and laymen. + We propose a new task of expertise style transfer and contribute a manually annotated + dataset with the goal of alleviating such cognitive biases. Solving this task not + only simplifies the professional language, but also improves the accuracy and expertise + level of laymen descriptions using simple words. This is a challenging task, unaddressed + in previous work, as it requires the models to have expert intelligence in order + to modify text with a deep understanding of domain knowledge and structures. We + establish the benchmark performance of five state-of-the-art models for style transfer + and text simplification. The results demonstrate a significant gap between machine + and human performance. We also discuss the challenges of automatic evaluation, to + provide insights into future research directions. The dataset is publicly available + at r̆lhttps://srhthu.github.io/expertise-style-transfer/. +links: +- name: URL + url: https://aclanthology.org/2020.acl-main.100 +--- diff --git a/content/publication/chandrasekaran-kan-2018-countering/cite.bib b/content/publication/chandrasekaran-kan-2018-countering/cite.bib new file mode 100644 index 0000000..e564431 --- /dev/null +++ b/content/publication/chandrasekaran-kan-2018-countering/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{chandrasekaran-kan-2018-countering, + abstract = {We systematically confirm that instructors are strongly influenced by the user interface presentation of Massive Online Open Course (MOOC) discussion forums. In a large scale dataset, we conclusively show that instructor interventions exhibit strong position bias, as measured by the position where the thread appeared on the user interface at the time of intervention. We measure and remove this bias, enabling unbiased statistical modelling and evaluation. We show that our de-biased classifier improves predicting interventions over the state-of-the-art on courses with sufficient number of interventions by 8.2% in F1 and 24.4% in recall on average.}, + address = {Melbourne, Australia}, + author = {Chandrasekaran, Muthu Kumar and +Kan, Min-Yen}, + booktitle = {Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications}, + doi = {10.18653/v1/W18-3720}, + editor = {Tseng, Yuen-Hsien and +Chen, Hsin-Hsi and +Ng, Vincent and +Komachi, Mamoru}, + month = {July}, + pages = {135--142}, + publisher = {Association for Computational Linguistics}, + title = {Countering Position Bias in Instructor Interventions in MOOC Discussion Forums}, + url = {https://aclanthology.org/W18-3720}, + year = {2018} +} diff --git a/content/publication/chandrasekaran-kan-2018-countering/index.md b/content/publication/chandrasekaran-kan-2018-countering/index.md new file mode 100644 index 0000000..96700b9 --- /dev/null +++ b/content/publication/chandrasekaran-kan-2018-countering/index.md @@ -0,0 +1,24 @@ +--- +title: Countering Position Bias in Instructor Interventions in MOOC Discussion Forums +authors: +- Muthu Kumar Chandrasekaran +- Min-Yen Kan +date: '2018-07-01' +publishDate: '2024-07-11T07:40:56.347855Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 5th Workshop on Natural Language Processing Techniques + for Educational Applications*' +doi: 10.18653/v1/W18-3720 +abstract: We systematically confirm that instructors are strongly influenced by the + user interface presentation of Massive Online Open Course (MOOC) discussion forums. + In a large scale dataset, we conclusively show that instructor interventions exhibit + strong position bias, as measured by the position where the thread appeared on the + user interface at the time of intervention. We measure and remove this bias, enabling + unbiased statistical modelling and evaluation. We show that our de-biased classifier + improves predicting interventions over the state-of-the-art on courses with sufficient + number of interventions by 8.2% in F1 and 24.4% in recall on average. +links: +- name: URL + url: https://aclanthology.org/W18-3720 +--- diff --git a/content/publication/chen-etal-2015-interactive/cite.bib b/content/publication/chen-etal-2015-interactive/cite.bib new file mode 100644 index 0000000..3eb7c92 --- /dev/null +++ b/content/publication/chen-etal-2015-interactive/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{chen-etal-2015-interactive, + address = {Beijing, China}, + author = {Chen, Tao and +Zheng, Naijia and +Zhao, Yue and +Chandrasekaran, Muthu Kumar and +Kan, Min-Yen}, + booktitle = {Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications}, + doi = {10.18653/v1/W15-4406}, + editor = {Chen, Hsin-Hsi and +Tseng, Yuen-Hsien and +Matsumoto, Yuji and +Wong, Lung Hsiang}, + month = {July}, + pages = {34--42}, + publisher = {Association for Computational Linguistics}, + title = {Interactive Second Language Learning from News Websites}, + url = {https://aclanthology.org/W15-4406}, + year = {2015} +} diff --git a/content/publication/chen-etal-2015-interactive/index.md b/content/publication/chen-etal-2015-interactive/index.md new file mode 100644 index 0000000..921265b --- /dev/null +++ b/content/publication/chen-etal-2015-interactive/index.md @@ -0,0 +1,19 @@ +--- +title: Interactive Second Language Learning from News Websites +authors: +- Tao Chen +- Naijia Zheng +- Yue Zhao +- Muthu Kumar Chandrasekaran +- Min-Yen Kan +date: '2015-07-01' +publishDate: '2024-07-11T07:40:56.401303Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2nd Workshop on Natural Language Processing Techniques + for Educational Applications*' +doi: 10.18653/v1/W15-4406 +links: +- name: URL + url: https://aclanthology.org/W15-4406 +--- diff --git a/content/publication/councill-etal-2008-parscit/cite.bib b/content/publication/councill-etal-2008-parscit/cite.bib new file mode 100644 index 0000000..88d06a2 --- /dev/null +++ b/content/publication/councill-etal-2008-parscit/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{councill-etal-2008-parscit, + abstract = {We describe ParsCit, a freely available, open-source implementation of a reference string parsing package. At the core of ParsCit is a trained conditional random field (CRF) model used to label the token sequences in the reference string. A heuristic model wraps this core with added functionality to identify reference strings from a plain text file, and to retrieve the citation contexts. The package comes with utilities to run it as a web service or as a standalone utility. We compare ParsCit on three distinct reference string datasets and show that it compares well with other previously published work.}, + address = {Marrakech, Morocco}, + author = {Councill, Isaac and +Giles, C. Lee and +Kan, Min-Yen}, + booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, + editor = {Calzolari, Nicoletta and +Choukri, Khalid and +Maegaard, Bente and +Mariani, Joseph and +Odijk, Jan and +Piperidis, Stelios and +Tapias, Daniel}, + month = {May}, + publisher = {European Language Resources Association (ELRA)}, + title = {ParsCit: an Open-source CRF Reference String Parsing Package}, + url = {http://www.lrec-conf.org/proceedings/lrec2008/pdf/166_paper.pdf}, + year = {2008} +} diff --git a/content/publication/councill-etal-2008-parscit/index.md b/content/publication/councill-etal-2008-parscit/index.md new file mode 100644 index 0000000..1b346a9 --- /dev/null +++ b/content/publication/councill-etal-2008-parscit/index.md @@ -0,0 +1,22 @@ +--- +title: 'ParsCit: an Open-source CRF Reference String Parsing Package' +authors: +- Isaac Councill +- C. Lee Giles +- Min-Yen Kan +date: '2008-05-01' +publishDate: '2024-07-11T07:40:56.561520Z' +publication_types: +- paper-conference +publication: "*Proceedings of the Sixth International Conference on Language Resources + and Evaluation (LREC'08)*" +abstract: We describe ParsCit, a freely available, open-source implementation of a + reference string parsing package. At the core of ParsCit is a trained conditional + random field (CRF) model used to label the token sequences in the reference string. + A heuristic model wraps this core with added functionality to identify reference + strings from a plain text file, and to retrieve the citation contexts. The package + comes with utilities to run it as a web service or as a standalone utility. We compare + ParsCit on three distinct reference string datasets and show that it compares well + with other previously published work. +url_pdf: http://www.lrec-conf.org/proceedings/lrec2008/pdf/166_paper.pdf +--- diff --git a/content/publication/elmacioglu-etal-2007-psnus/cite.bib b/content/publication/elmacioglu-etal-2007-psnus/cite.bib new file mode 100644 index 0000000..e8215c0 --- /dev/null +++ b/content/publication/elmacioglu-etal-2007-psnus/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{elmacioglu-etal-2007-psnus, + address = {Prague, Czech Republic}, + author = {Elmacioglu, Ergin and +Tan, Yee Fan and +Yan, Su and +Kan, Min-Yen and +Lee, Dongwon}, + booktitle = {Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)}, + editor = {Agirre, Eneko and +Màrquez, Lluı́s and +Wicentowski, Richard}, + month = {June}, + pages = {268--271}, + publisher = {Association for Computational Linguistics}, + title = {PSNUS: Web People Name Disambiguation by Simple Clustering with Rich Features}, + url = {https://aclanthology.org/S07-1058}, + year = {2007} +} diff --git a/content/publication/elmacioglu-etal-2007-psnus/index.md b/content/publication/elmacioglu-etal-2007-psnus/index.md new file mode 100644 index 0000000..f61cad8 --- /dev/null +++ b/content/publication/elmacioglu-etal-2007-psnus/index.md @@ -0,0 +1,18 @@ +--- +title: 'PSNUS: Web People Name Disambiguation by Simple Clustering with Rich Features' +authors: +- Ergin Elmacioglu +- Yee Fan Tan +- Su Yan +- Min-Yen Kan +- Dongwon Lee +date: '2007-06-01' +publishDate: '2024-07-11T07:40:56.579914Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Fourth International Workshop on Semantic Evaluations + (SemEval-2007)*' +links: +- name: URL + url: https://aclanthology.org/S07-1058 +--- diff --git a/content/publication/gildea-etal-2018-acl/cite.bib b/content/publication/gildea-etal-2018-acl/cite.bib new file mode 100644 index 0000000..09ae4cf --- /dev/null +++ b/content/publication/gildea-etal-2018-acl/cite.bib @@ -0,0 +1,21 @@ +@inproceedings{gildea-etal-2018-acl, + abstract = {The Association of Computational Linguistic′s Anthology is the open source archive, and the main source for computational linguistics and natural language processing′s scientific literature. The ACL Anthology is currently maintained exclusively by community volunteers and has to be available and up-to-date at all times. We first discuss the current, open source approach used to achieve this, and then discuss how the planned use of Docker images will improve the Anthology′s long-term stability. This change will make it easier for researchers to utilize Anthology data for experimentation. We believe the ACL community can directly benefit from the extension-friendly architecture of the Anthology. We end by issuing an open challenge of reviewer matching we encourage the community to rally towards.}, + address = {Melbourne, Australia}, + author = {Gildea, Daniel and +Kan, Min-Yen and +Madnani, Nitin and +Teichmann, Christoph and +Villalba, Martı́n}, + booktitle = {Proceedings of Workshop for NLP Open Source Software (NLP-OSS)}, + doi = {10.18653/v1/W18-2504}, + editor = {Park, Eunjeong L. and +Hagiwara, Masato and +Milajevs, Dmitrijs and +Tan, Liling}, + month = {July}, + pages = {23--28}, + publisher = {Association for Computational Linguistics}, + title = {The ACL Anthology: Current State and Future Directions}, + url = {https://aclanthology.org/W18-2504}, + year = {2018} +} diff --git a/content/publication/gildea-etal-2018-acl/index.md b/content/publication/gildea-etal-2018-acl/index.md new file mode 100644 index 0000000..ebeaabc --- /dev/null +++ b/content/publication/gildea-etal-2018-acl/index.md @@ -0,0 +1,28 @@ +--- +title: 'The ACL Anthology: Current State and Future Directions' +authors: +- Daniel Gildea +- Min-Yen Kan +- Nitin Madnani +- Christoph Teichmann +- Martı́n Villalba +date: '2018-07-01' +publishDate: '2024-07-11T07:40:56.354411Z' +publication_types: +- paper-conference +publication: '*Proceedings of Workshop for NLP Open Source Software (NLP-OSS)*' +doi: 10.18653/v1/W18-2504 +abstract: The Association of Computational Linguistic′s Anthology is the open source + archive, and the main source for computational linguistics and natural language + processing′s scientific literature. The ACL Anthology is currently maintained exclusively + by community volunteers and has to be available and up-to-date at all times. We + first discuss the current, open source approach used to achieve this, and then discuss + how the planned use of Docker images will improve the Anthology′s long-term stability. + This change will make it easier for researchers to utilize Anthology data for experimentation. + We believe the ACL community can directly benefit from the extension-friendly architecture + of the Anthology. We end by issuing an open challenge of reviewer matching we encourage + the community to rally towards. +links: +- name: URL + url: https://aclanthology.org/W18-2504 +--- diff --git a/content/publication/halder-etal-2017-modeling/cite.bib b/content/publication/halder-etal-2017-modeling/cite.bib new file mode 100644 index 0000000..f6bdf6d --- /dev/null +++ b/content/publication/halder-etal-2017-modeling/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{halder-etal-2017-modeling, + abstract = {Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual′s posts. We analyze a real-world dataset from the Mental Health section of HealthBoards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely. We study the problem of predicting a patient′s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.}, + address = {Copenhagen, Denmark}, + author = {Halder, Kishaloy and +Poddar, Lahari and +Kan, Min-Yen}, + booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis}, + doi = {10.18653/v1/W17-5217}, + editor = {Balahur, Alexandra and +Mohammad, Saif M. and +van der Goot, Erik}, + month = {September}, + pages = {127--135}, + publisher = {Association for Computational Linguistics}, + title = {Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach}, + url = {https://aclanthology.org/W17-5217}, + year = {2017} +} diff --git a/content/publication/halder-etal-2017-modeling/index.md b/content/publication/halder-etal-2017-modeling/index.md new file mode 100644 index 0000000..53f4e0c --- /dev/null +++ b/content/publication/halder-etal-2017-modeling/index.md @@ -0,0 +1,30 @@ +--- +title: 'Modeling Temporal Progression of Emotional Status in Mental Health Forum: + A Recurrent Neural Net Approach' +authors: +- Kishaloy Halder +- Lahari Poddar +- Min-Yen Kan +date: '2017-09-01' +publishDate: '2024-07-11T07:40:56.375219Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, + Sentiment and Social Media Analysis*' +doi: 10.18653/v1/W17-5217 +abstract: Patients turn to Online Health Communities not only for information on specific + conditions but also for emotional support. Previous research has indicated that + the progression of emotional status can be studied through the linguistic patterns + of an individual′s posts. We analyze a real-world dataset from the Mental Health + section of HealthBoards.com. Estimated from the word usages in their posts, we find + that the emotional progress across patients vary widely. We study the problem of + predicting a patient′s emotional status in the future from her past posts and we + propose a Recurrent Neural Network (RNN) based architecture to address it. We find + that the future emotional status can be predicted with reasonable accuracy given + her historical posts and participation features. Our evaluation results demonstrate + the efficacy of our proposed architecture, by outperforming state-of-the-art approaches + with over 0.13 reduction in Mean Absolute Error. +links: +- name: URL + url: https://aclanthology.org/W17-5217 +--- diff --git a/content/publication/halder-etal-2019-predicting/cite.bib b/content/publication/halder-etal-2019-predicting/cite.bib new file mode 100644 index 0000000..ec8cf42 --- /dev/null +++ b/content/publication/halder-etal-2019-predicting/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{halder-etal-2019-predicting, + abstract = {Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or by sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread to help users comprehend long running discussion threads, which often contain repetitive or irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.}, + address = {Minneapolis, Minnesota}, + author = {Halder, Kishaloy and +Kan, Min-Yen and +Sugiyama, Kazunari}, + booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)}, + doi = {10.18653/v1/N19-1318}, + editor = {Burstein, Jill and +Doran, Christy and +Solorio, Thamar}, + month = {June}, + pages = {3148--3157}, + publisher = {Association for Computational Linguistics}, + title = {Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture}, + url = {https://aclanthology.org/N19-1318}, + year = {2019} +} diff --git a/content/publication/halder-etal-2019-predicting/index.md b/content/publication/halder-etal-2019-predicting/index.md new file mode 100644 index 0000000..de5cf85 --- /dev/null +++ b/content/publication/halder-etal-2019-predicting/index.md @@ -0,0 +1,31 @@ +--- +title: 'Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture' +authors: +- Kishaloy Halder +- Min-Yen Kan +- Kazunari Sugiyama +date: '2019-06-01' +publishDate: '2024-07-11T07:40:56.327069Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2019 Conference of the North American Chapter of + the Association for Computational Linguistics: Human Language Technologies, Volume + 1 (Long and Short Papers)*' +doi: 10.18653/v1/N19-1318 +abstract: Users participate in online discussion forums to learn from others and share + their knowledge with the community. They often start a thread with a question or + by sharing their new findings on a certain topic. We find that, unlike Community + Question Answering, where questions are mostly factoid based, the threads in a forum + are often open-ended (e.g., asking for recommendations from others) without a single + correct answer. In this paper, we address the task of identifying helpful posts + in a forum thread to help users comprehend long running discussion threads, which + often contain repetitive or irrelevant posts. We propose a recurrent neural network + based architecture to model (i) the relevance of a post regarding the original post + starting the thread and (ii) the novelty it brings to the discussion, compared to + the previous posts in the thread. Experimental results on different types of online + forum datasets show that our model significantly outperforms the state-of-the-art + neural network models for text classification. +links: +- name: URL + url: https://aclanthology.org/N19-1318 +--- diff --git a/content/publication/hoang-etal-2009-examination/cite.bib b/content/publication/hoang-etal-2009-examination/cite.bib new file mode 100644 index 0000000..c39ec7c --- /dev/null +++ b/content/publication/hoang-etal-2009-examination/cite.bib @@ -0,0 +1,17 @@ +@inproceedings{hoang-etal-2009-examination, + address = {Singapore}, + author = {Hoang, Hung Huu and +Kim, Su Nam and +Kan, Min-Yen}, + booktitle = {Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications (MWE 2009)}, + editor = {Anastasiou, Dimitra and +Hashimoto, Chikara and +Nakov, Preslav and +Kim, Su Nam}, + month = {August}, + pages = {31--39}, + publisher = {Association for Computational Linguistics}, + title = {A re-examination of lexical association measures}, + url = {https://aclanthology.org/W09-2905}, + year = {2009} +} diff --git a/content/publication/hoang-etal-2009-examination/index.md b/content/publication/hoang-etal-2009-examination/index.md new file mode 100644 index 0000000..0fdea75 --- /dev/null +++ b/content/publication/hoang-etal-2009-examination/index.md @@ -0,0 +1,16 @@ +--- +title: A re-examination of lexical association measures +authors: +- Hung Huu Hoang +- Su Nam Kim +- Min-Yen Kan +date: '2009-08-01' +publishDate: '2024-07-11T07:40:56.530100Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Workshop on Multiword Expressions: Identification, + Interpretation, Disambiguation and Applications (MWE 2009)*' +links: +- name: URL + url: https://aclanthology.org/W09-2905 +--- diff --git a/content/publication/hoang-kan-2010-towards/cite.bib b/content/publication/hoang-kan-2010-towards/cite.bib new file mode 100644 index 0000000..870bd89 --- /dev/null +++ b/content/publication/hoang-kan-2010-towards/cite.bib @@ -0,0 +1,14 @@ +@inproceedings{hoang-kan-2010-towards, + address = {Beijing, China}, + author = {Hoang, Cong Duy Vu and +Kan, Min-Yen}, + booktitle = {Coling 2010: Posters}, + editor = {Huang, Chu-Ren and +Jurafsky, Dan}, + month = {August}, + pages = {427--435}, + publisher = {Coling 2010 Organizing Committee}, + title = {Towards Automated Related Work Summarization}, + url = {https://aclanthology.org/C10-2049}, + year = {2010} +} diff --git a/content/publication/hoang-kan-2010-towards/index.md b/content/publication/hoang-kan-2010-towards/index.md new file mode 100644 index 0000000..610441a --- /dev/null +++ b/content/publication/hoang-kan-2010-towards/index.md @@ -0,0 +1,14 @@ +--- +title: Towards Automated Related Work Summarization +authors: +- Cong Duy Vu Hoang +- Min-Yen Kan +date: '2010-08-01' +publishDate: '2024-07-11T07:40:56.499873Z' +publication_types: +- paper-conference +publication: '*Coling 2010: Posters*' +links: +- name: URL + url: https://aclanthology.org/C10-2049 +--- diff --git a/content/publication/jiang-etal-2018-identifying/cite.bib b/content/publication/jiang-etal-2018-identifying/cite.bib new file mode 100644 index 0000000..9526671 --- /dev/null +++ b/content/publication/jiang-etal-2018-identifying/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{jiang-etal-2018-identifying, + abstract = {Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield insight on developing topics. We propose an embedded trend detection framework for this task which incorporates our bijunctive hypothesis that important phrases are written by important authors within a field and vice versa. By ranking both author and phrase information in a multigraph, our method jointly determines key phrases and authoritative authors. We represent this intermediate output as phrasal embeddings, and feed this to a recurrent neural network (RNN) to compute trend scores that identify research trends. Over two large datasets of scientific articles, we demonstrate that our approach successfully detects past trends from the field, outperforming baselines based solely on text centrality or citation.}, + address = {Santa Fe, New Mexico, USA}, + author = {Jiang, Shenhao and +Prasad, Animesh and +Kan, Min-Yen and +Sugiyama, Kazunari}, + booktitle = {Proceedings of the 27th International Conference on Computational Linguistics}, + editor = {Bender, Emily M. and +Derczynski, Leon and +Isabelle, Pierre}, + month = {August}, + pages = {259--269}, + publisher = {Association for Computational Linguistics}, + title = {Identifying Emergent Research Trends by Key Authors and Phrases}, + url = {https://aclanthology.org/C18-1022}, + year = {2018} +} diff --git a/content/publication/jiang-etal-2018-identifying/index.md b/content/publication/jiang-etal-2018-identifying/index.md new file mode 100644 index 0000000..e7db811 --- /dev/null +++ b/content/publication/jiang-etal-2018-identifying/index.md @@ -0,0 +1,28 @@ +--- +title: Identifying Emergent Research Trends by Key Authors and Phrases +authors: +- Shenhao Jiang +- Animesh Prasad +- Min-Yen Kan +- Kazunari Sugiyama +date: '2018-08-01' +publishDate: '2024-07-11T07:40:56.368249Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 27th International Conference on Computational Linguistics*' +abstract: Identifying emergent research trends is a key issue for both primary researchers + as well as secondary research managers. Such processes can uncover the historical + development of an area, and yield insight on developing topics. We propose an embedded + trend detection framework for this task which incorporates our bijunctive hypothesis + that important phrases are written by important authors within a field and vice + versa. By ranking both author and phrase information in a multigraph, our method + jointly determines key phrases and authoritative authors. We represent this intermediate + output as phrasal embeddings, and feed this to a recurrent neural network (RNN) + to compute trend scores that identify research trends. Over two large datasets of + scientific articles, we demonstrate that our approach successfully detects past + trends from the field, outperforming baselines based solely on text centrality or + citation. +links: +- name: URL + url: https://aclanthology.org/C18-1022 +--- diff --git a/content/publication/jin-etal-2013-mining/cite.bib b/content/publication/jin-etal-2013-mining/cite.bib new file mode 100644 index 0000000..3817a6a --- /dev/null +++ b/content/publication/jin-etal-2013-mining/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{jin-etal-2013-mining, + address = {Seattle, Washington, USA}, + author = {Jin, Yiping and +Kan, Min-Yen and +Ng, Jun-Ping and +He, Xiangnan}, + booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing}, + editor = {Yarowsky, David and +Baldwin, Timothy and +Korhonen, Anna and +Livescu, Karen and +Bethard, Steven}, + month = {October}, + pages = {780--790}, + publisher = {Association for Computational Linguistics}, + title = {Mining Scientific Terms and their Definitions: A Study of the ACL Anthology}, + url = {https://aclanthology.org/D13-1073}, + year = {2013} +} diff --git a/content/publication/jin-etal-2013-mining/index.md b/content/publication/jin-etal-2013-mining/index.md new file mode 100644 index 0000000..07ad51a --- /dev/null +++ b/content/publication/jin-etal-2013-mining/index.md @@ -0,0 +1,17 @@ +--- +title: 'Mining Scientific Terms and their Definitions: A Study of the ACL Anthology' +authors: +- Yiping Jin +- Min-Yen Kan +- Jun-Ping Ng +- Xiangnan He +date: '2013-10-01' +publishDate: '2024-07-11T07:40:56.438607Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2013 Conference on Empirical Methods in Natural + Language Processing*' +links: +- name: URL + url: https://aclanthology.org/D13-1073 +--- diff --git a/content/publication/kan-2015-keywords/cite.bib b/content/publication/kan-2015-keywords/cite.bib new file mode 100644 index 0000000..e4c010d --- /dev/null +++ b/content/publication/kan-2015-keywords/cite.bib @@ -0,0 +1,16 @@ +@inproceedings{kan-2015-keywords, + address = {Beijing, China}, + author = {Kan, Min-Yen}, + booktitle = {Proceedings of the ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction}, + doi = {10.18653/v1/W15-3601}, + editor = {Gollapalli, Sujatha Das and +Caragea, Cornelia and +Li, Xiaoli and +Giles, C. Lee}, + month = {July}, + pages = {1}, + publisher = {Association for Computational Linguistics}, + title = {Keywords, phrases, clauses and sentences: topicality, indicativeness and informativeness at scales}, + url = {https://aclanthology.org/W15-3601}, + year = {2015} +} diff --git a/content/publication/kan-2015-keywords/index.md b/content/publication/kan-2015-keywords/index.md new file mode 100644 index 0000000..09138bc --- /dev/null +++ b/content/publication/kan-2015-keywords/index.md @@ -0,0 +1,16 @@ +--- +title: 'Keywords, phrases, clauses and sentences: topicality, indicativeness and informativeness + at scales' +authors: +- Min-Yen Kan +date: '2015-07-01' +publishDate: '2024-07-11T07:40:56.407539Z' +publication_types: +- paper-conference +publication: '*Proceedings of the ACL 2015 Workshop on Novel Computational Approaches + to Keyphrase Extraction*' +doi: 10.18653/v1/W15-3601 +links: +- name: URL + url: https://aclanthology.org/W15-3601 +--- diff --git a/content/publication/kan-etal-1998-linear/cite.bib b/content/publication/kan-etal-1998-linear/cite.bib new file mode 100644 index 0000000..f0d6790 --- /dev/null +++ b/content/publication/kan-etal-1998-linear/cite.bib @@ -0,0 +1,9 @@ +@inproceedings{kan-etal-1998-linear, + author = {Kan, Min-Yen and +Klavans, Judith L. and +McKeown, Kathleen R.}, + booktitle = {Sixth Workshop on Very Large Corpora}, + title = {Linear Segmentation and Segment Significance}, + url = {https://aclanthology.org/W98-1123}, + year = {1998} +} diff --git a/content/publication/kan-etal-1998-linear/index.md b/content/publication/kan-etal-1998-linear/index.md new file mode 100644 index 0000000..161d6bd --- /dev/null +++ b/content/publication/kan-etal-1998-linear/index.md @@ -0,0 +1,15 @@ +--- +title: Linear Segmentation and Segment Significance +authors: +- Min-Yen Kan +- Judith L. Klavans +- Kathleen R. McKeown +date: '1998-01-01' +publishDate: '2024-07-11T07:40:56.632433Z' +publication_types: +- paper-conference +publication: '*Sixth Workshop on Very Large Corpora*' +links: +- name: URL + url: https://aclanthology.org/W98-1123 +--- diff --git a/content/publication/kan-etal-2001-applying/cite.bib b/content/publication/kan-etal-2001-applying/cite.bib new file mode 100644 index 0000000..f5b920a --- /dev/null +++ b/content/publication/kan-etal-2001-applying/cite.bib @@ -0,0 +1,14 @@ +@inproceedings{kan-etal-2001-applying, + address = {Toulouse, France}, + author = {Kan, Min-Yen and +McKeown, Kathleen R. and +Klavans, Judith L.}, + booktitle = {Proceedings of the ACL 2001 Eighth European Workshop on Natural Language Generation (EWNLG)}, + editor = {Horacek, Helmut and +Nicolov, Nicolas and +Wanner, Leo}, + publisher = {Association for Computational Linguistics}, + title = {Applying Natural Language Generation to Indicative Summarization}, + url = {https://aclanthology.org/W01-0813}, + year = {2001} +} diff --git a/content/publication/kan-etal-2001-applying/index.md b/content/publication/kan-etal-2001-applying/index.md new file mode 100644 index 0000000..d7ff2ce --- /dev/null +++ b/content/publication/kan-etal-2001-applying/index.md @@ -0,0 +1,16 @@ +--- +title: Applying Natural Language Generation to Indicative Summarization +authors: +- Min-Yen Kan +- Kathleen R. McKeown +- Judith L. Klavans +date: '2001-01-01' +publishDate: '2024-07-11T07:40:56.622249Z' +publication_types: +- paper-conference +publication: '*Proceedings of the ACL 2001 Eighth European Workshop on Natural Language + Generation (EWNLG)*' +links: +- name: URL + url: https://aclanthology.org/W01-0813 +--- diff --git a/content/publication/kan-etal-2002-using/cite.bib b/content/publication/kan-etal-2002-using/cite.bib new file mode 100644 index 0000000..0b972b7 --- /dev/null +++ b/content/publication/kan-etal-2002-using/cite.bib @@ -0,0 +1,14 @@ +@inproceedings{kan-etal-2002-using, + address = {Las Palmas, Canary Islands - Spain}, + author = {Kan, Min-Yen and +Klavans, Judith L. and +McKeown, Kathleen R.}, + booktitle = {Proceedings of the Third International Conference on Language Resources and Evaluation (LREC′02)}, + editor = {González Rodrı́guez, Manuel and +Suarez Araujo, Carmen Paz}, + month = {May}, + publisher = {European Language Resources Association (ELRA)}, + title = {Using the Annotated Bibliography as a Resource for Indicative Summarization}, + url = {http://www.lrec-conf.org/proceedings/lrec2002/pdf/7.pdf}, + year = {2002} +} diff --git a/content/publication/kan-etal-2002-using/index.md b/content/publication/kan-etal-2002-using/index.md new file mode 100644 index 0000000..d463bda --- /dev/null +++ b/content/publication/kan-etal-2002-using/index.md @@ -0,0 +1,14 @@ +--- +title: Using the Annotated Bibliography as a Resource for Indicative Summarization +authors: +- Min-Yen Kan +- Judith L. Klavans +- Kathleen R. McKeown +date: '2002-05-01' +publishDate: '2024-07-11T07:40:56.616248Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Third International Conference on Language Resources + and Evaluation (LREC′02)*' +url_pdf: http://www.lrec-conf.org/proceedings/lrec2002/pdf/7.pdf +--- diff --git a/content/publication/kan-mckeown-2002-corpus/cite.bib b/content/publication/kan-mckeown-2002-corpus/cite.bib new file mode 100644 index 0000000..7724024 --- /dev/null +++ b/content/publication/kan-mckeown-2002-corpus/cite.bib @@ -0,0 +1,13 @@ +@inproceedings{kan-mckeown-2002-corpus, + address = {Harriman, New York, USA}, + author = {Kan, Min-Yen and +McKeown, Kathleen R.}, + booktitle = {Proceedings of the International Natural Language Generation Conference}, + editor = {McKeown, Kathleen}, + month = {July}, + pages = {1--8}, + publisher = {Association for Computational Linguistics}, + title = {Corpus-trained Text Generation for Summarization}, + url = {https://aclanthology.org/W02-2101}, + year = {2002} +} diff --git a/content/publication/kan-mckeown-2002-corpus/index.md b/content/publication/kan-mckeown-2002-corpus/index.md new file mode 100644 index 0000000..de4c1b4 --- /dev/null +++ b/content/publication/kan-mckeown-2002-corpus/index.md @@ -0,0 +1,14 @@ +--- +title: Corpus-trained Text Generation for Summarization +authors: +- Min-Yen Kan +- Kathleen R. McKeown +date: '2002-07-01' +publishDate: '2024-07-11T07:40:56.610197Z' +publication_types: +- paper-conference +publication: '*Proceedings of the International Natural Language Generation Conference*' +links: +- name: URL + url: https://aclanthology.org/W02-2101 +--- diff --git a/content/publication/kim-etal-2009-extracting/cite.bib b/content/publication/kim-etal-2009-extracting/cite.bib new file mode 100644 index 0000000..eddf76c --- /dev/null +++ b/content/publication/kim-etal-2009-extracting/cite.bib @@ -0,0 +1,14 @@ +@inproceedings{kim-etal-2009-extracting, + address = {Sydney, Australia}, + author = {Kim, Su Nam and +Baldwin, Timothy and +Kan, Min-Yen}, + booktitle = {Proceedings of the Australasian Language Technology Association Workshop 2009}, + editor = {Pizzato, Luiz Augusto and +Schwitter, Rolf}, + month = {December}, + pages = {94--98}, + title = {Extracting Domain-Specific Words - A Statistical Approach}, + url = {https://aclanthology.org/U09-1013}, + year = {2009} +} diff --git a/content/publication/kim-etal-2009-extracting/index.md b/content/publication/kim-etal-2009-extracting/index.md new file mode 100644 index 0000000..c4f75e6 --- /dev/null +++ b/content/publication/kim-etal-2009-extracting/index.md @@ -0,0 +1,16 @@ +--- +title: Extracting Domain-Specific Words - A Statistical Approach +authors: +- Su Nam Kim +- Timothy Baldwin +- Min-Yen Kan +date: '2009-12-01' +publishDate: '2024-07-11T07:40:56.536274Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Australasian Language Technology Association Workshop + 2009*' +links: +- name: URL + url: https://aclanthology.org/U09-1013 +--- diff --git a/content/publication/kim-etal-2010-evaluating/cite.bib b/content/publication/kim-etal-2010-evaluating/cite.bib new file mode 100644 index 0000000..e93eb27 --- /dev/null +++ b/content/publication/kim-etal-2010-evaluating/cite.bib @@ -0,0 +1,15 @@ +@inproceedings{kim-etal-2010-evaluating, + address = {Beijing, China}, + author = {Kim, Su Nam and +Baldwin, Timothy and +Kan, Min-Yen}, + booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)}, + editor = {Huang, Chu-Ren and +Jurafsky, Dan}, + month = {August}, + pages = {572--580}, + publisher = {Coling 2010 Organizing Committee}, + title = {Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extraction}, + url = {https://aclanthology.org/C10-1065}, + year = {2010} +} diff --git a/content/publication/kim-etal-2010-evaluating/index.md b/content/publication/kim-etal-2010-evaluating/index.md new file mode 100644 index 0000000..e5a8159 --- /dev/null +++ b/content/publication/kim-etal-2010-evaluating/index.md @@ -0,0 +1,16 @@ +--- +title: Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extraction +authors: +- Su Nam Kim +- Timothy Baldwin +- Min-Yen Kan +date: '2010-08-01' +publishDate: '2024-07-11T07:40:56.505844Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 23rd International Conference on Computational Linguistics + (Coling 2010)*' +links: +- name: URL + url: https://aclanthology.org/C10-1065 +--- diff --git a/content/publication/kim-etal-2010-semeval/cite.bib b/content/publication/kim-etal-2010-semeval/cite.bib new file mode 100644 index 0000000..1c33576 --- /dev/null +++ b/content/publication/kim-etal-2010-semeval/cite.bib @@ -0,0 +1,16 @@ +@inproceedings{kim-etal-2010-semeval, + address = {Uppsala, Sweden}, + author = {Kim, Su Nam and +Medelyan, Olena and +Kan, Min-Yen and +Baldwin, Timothy}, + booktitle = {Proceedings of the 5th International Workshop on Semantic Evaluation}, + editor = {Erk, Katrin and +Strapparava, Carlo}, + month = {July}, + pages = {21--26}, + publisher = {Association for Computational Linguistics}, + title = {SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles}, + url = {https://aclanthology.org/S10-1004}, + year = {2010} +} diff --git a/content/publication/kim-etal-2010-semeval/index.md b/content/publication/kim-etal-2010-semeval/index.md new file mode 100644 index 0000000..a76c90b --- /dev/null +++ b/content/publication/kim-etal-2010-semeval/index.md @@ -0,0 +1,16 @@ +--- +title: 'SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles' +authors: +- Su Nam Kim +- Olena Medelyan +- Min-Yen Kan +- Timothy Baldwin +date: '2010-07-01' +publishDate: '2024-07-11T07:40:56.487584Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 5th International Workshop on Semantic Evaluation*' +links: +- name: URL + url: https://aclanthology.org/S10-1004 +--- diff --git a/content/publication/kim-kan-2009-examining/cite.bib b/content/publication/kim-kan-2009-examining/cite.bib new file mode 100644 index 0000000..359245c --- /dev/null +++ b/content/publication/kim-kan-2009-examining/cite.bib @@ -0,0 +1,16 @@ +@inproceedings{kim-kan-2009-examining, + address = {Singapore}, + author = {Kim, Su Nam and +Kan, Min-Yen}, + booktitle = {Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications (MWE 2009)}, + editor = {Anastasiou, Dimitra and +Hashimoto, Chikara and +Nakov, Preslav and +Kim, Su Nam}, + month = {August}, + pages = {9--16}, + publisher = {Association for Computational Linguistics}, + title = {Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles}, + url = {https://aclanthology.org/W09-2902}, + year = {2009} +} diff --git a/content/publication/kim-kan-2009-examining/index.md b/content/publication/kim-kan-2009-examining/index.md new file mode 100644 index 0000000..656dd4a --- /dev/null +++ b/content/publication/kim-kan-2009-examining/index.md @@ -0,0 +1,15 @@ +--- +title: Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles +authors: +- Su Nam Kim +- Min-Yen Kan +date: '2009-08-01' +publishDate: '2024-07-11T07:40:56.524050Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Workshop on Multiword Expressions: Identification, + Interpretation, Disambiguation and Applications (MWE 2009)*' +links: +- name: URL + url: https://aclanthology.org/W09-2902 +--- diff --git a/content/publication/klavans-kan-1998-role-verbs/cite.bib b/content/publication/klavans-kan-1998-role-verbs/cite.bib new file mode 100644 index 0000000..dcde309 --- /dev/null +++ b/content/publication/klavans-kan-1998-role-verbs/cite.bib @@ -0,0 +1,13 @@ +@inproceedings{klavans-kan-1998-role-verbs, + address = {Montreal, Quebec, Canada}, + author = {Klavans, Judith L. and +Kan, Min-Yen}, + booktitle = {36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1}, + doi = {10.3115/980845.980959}, + month = {August}, + pages = {680--686}, + publisher = {Association for Computational Linguistics}, + title = {Role of Verbs in Document Analysis}, + url = {https://aclanthology.org/P98-1112}, + year = {1998} +} diff --git a/content/publication/klavans-kan-1998-role-verbs/index.md b/content/publication/klavans-kan-1998-role-verbs/index.md new file mode 100644 index 0000000..a8357e2 --- /dev/null +++ b/content/publication/klavans-kan-1998-role-verbs/index.md @@ -0,0 +1,16 @@ +--- +title: Role of Verbs in Document Analysis +authors: +- Judith L. Klavans +- Min-Yen Kan +date: '1998-08-01' +publishDate: '2024-07-11T07:40:56.643091Z' +publication_types: +- paper-conference +publication: '*36th Annual Meeting of the Association for Computational Linguistics + and 17th International Conference on Computational Linguistics, Volume 1*' +doi: 10.3115/980845.980959 +links: +- name: URL + url: https://aclanthology.org/P98-1112 +--- diff --git a/content/publication/lei-etal-2018-sequicity/cite.bib b/content/publication/lei-etal-2018-sequicity/cite.bib new file mode 100644 index 0000000..54d0742 --- /dev/null +++ b/content/publication/lei-etal-2018-sequicity/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{lei-etal-2018-sequicity, + abstract = {Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.}, + address = {Melbourne, Australia}, + author = {Lei, Wenqiang and +Jin, Xisen and +Kan, Min-Yen and +Ren, Zhaochun and +He, Xiangnan and +Yin, Dawei}, + booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.18653/v1/P18-1133}, + editor = {Gurevych, Iryna and +Miyao, Yusuke}, + month = {July}, + pages = {1437--1447}, + publisher = {Association for Computational Linguistics}, + title = {Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures}, + url = {https://aclanthology.org/P18-1133}, + year = {2018} +} diff --git a/content/publication/lei-etal-2018-sequicity/index.md b/content/publication/lei-etal-2018-sequicity/index.md new file mode 100644 index 0000000..d00d5c1 --- /dev/null +++ b/content/publication/lei-etal-2018-sequicity/index.md @@ -0,0 +1,32 @@ +--- +title: 'Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence + Architectures' +authors: +- Wenqiang Lei +- Xisen Jin +- Min-Yen Kan +- Zhaochun Ren +- Xiangnan He +- Dawei Yin +date: '2018-07-01' +publishDate: '2024-07-11T07:40:56.361300Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 56th Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +doi: 10.18653/v1/P18-1133 +abstract: 'Existing solutions to task-oriented dialogue systems follow pipeline designs + which introduces architectural complexity and fragility. We propose a novel, holistic, + extendable framework based on a single sequence-to-sequence (seq2seq) model which + can be optimized with supervised or reinforcement learning. A key contribution is + that we design text spans named belief spans to track dialogue believes, allowing + task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we + propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: + significantly reducing model complexity in terms of number of parameters and training + time by a magnitude. It significantly outperforms state-of-the-art pipeline-based + methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary + (OOV) cases where pipeline-designed competitors totally fail.' +links: +- name: URL + url: https://aclanthology.org/P18-1133 +--- diff --git a/content/publication/lei-etal-2020-examining/cite.bib b/content/publication/lei-etal-2020-examining/cite.bib new file mode 100644 index 0000000..7777c7e --- /dev/null +++ b/content/publication/lei-etal-2020-examining/cite.bib @@ -0,0 +1,23 @@ +@inproceedings{lei-etal-2020-examining, + abstract = {In existing sophisticated text-to-SQL models, schema linking is often considered as a simple, minor component, belying its importance. By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking. We also build a simple BERT-based baseline, called Schema-Linking SQL (SLSQL) to perform a data-driven study. We find when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity. Many remaining errors are attributable to corpus noise. This suggests schema linking is the crux for the current text-to-SQL task. Our analytic studies provide insights on the characteristics of schema linking for future developments of text-to-SQL tasks.}, + address = {Online}, + author = {Lei, Wenqiang and +Wang, Weixin and +Ma, Zhixin and +Gan, Tian and +Lu, Wei and +Kan, Min-Yen and +Chua, Tat-Seng}, + booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, + doi = {10.18653/v1/2020.emnlp-main.564}, + editor = {Webber, Bonnie and +Cohn, Trevor and +He, Yulan and +Liu, Yang}, + month = {November}, + pages = {6943--6954}, + publisher = {Association for Computational Linguistics}, + title = {Re-examining the Role of Schema Linking in Text-to-SQL}, + url = {https://aclanthology.org/2020.emnlp-main.564}, + year = {2020} +} diff --git a/content/publication/lei-etal-2020-examining/index.md b/content/publication/lei-etal-2020-examining/index.md new file mode 100644 index 0000000..f5d759f --- /dev/null +++ b/content/publication/lei-etal-2020-examining/index.md @@ -0,0 +1,31 @@ +--- +title: Re-examining the Role of Schema Linking in Text-to-SQL +authors: +- Wenqiang Lei +- Weixin Wang +- Zhixin Ma +- Tian Gan +- Wei Lu +- Min-Yen Kan +- Tat-Seng Chua +date: '2020-11-01' +publishDate: '2024-07-11T07:40:56.269384Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2020 Conference on Empirical Methods in Natural + Language Processing (EMNLP)*' +doi: 10.18653/v1/2020.emnlp-main.564 +abstract: In existing sophisticated text-to-SQL models, schema linking is often considered + as a simple, minor component, belying its importance. By providing a schema linking + corpus based on the Spider text-to-SQL dataset, we systematically study the role + of schema linking. We also build a simple BERT-based baseline, called Schema-Linking + SQL (SLSQL) to perform a data-driven study. We find when schema linking is done + well, SLSQL demonstrates good performance on Spider despite its structural simplicity. + Many remaining errors are attributable to corpus noise. This suggests schema linking + is the crux for the current text-to-SQL task. Our analytic studies provide insights + on the characteristics of schema linking for future developments of text-to-SQL + tasks. +links: +- name: URL + url: https://aclanthology.org/2020.emnlp-main.564 +--- diff --git a/content/publication/li-etal-2020-molweni/cite.bib b/content/publication/li-etal-2020-molweni/cite.bib new file mode 100644 index 0000000..ff5f44e --- /dev/null +++ b/content/publication/li-etal-2020-molweni/cite.bib @@ -0,0 +1,23 @@ +@inproceedings{li-etal-2020-molweni, + abstract = {Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni′s source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions. Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing. Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7% F1 on Molweni′s questions, a 20+% significant drop as compared against its SQuAD 2.0 performance.}, + address = {Barcelona, Spain (Online)}, + author = {Li, Jiaqi and +Liu, Ming and +Kan, Min-Yen and +Zheng, Zihao and +Wang, Zekun and +Lei, Wenqiang and +Liu, Ting and +Qin, Bing}, + booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, + doi = {10.18653/v1/2020.coling-main.238}, + editor = {Scott, Donia and +Bel, Nuria and +Zong, Chengqing}, + month = {December}, + pages = {2642--2652}, + publisher = {International Committee on Computational Linguistics}, + title = {Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure}, + url = {https://aclanthology.org/2020.coling-main.238}, + year = {2020} +} diff --git a/content/publication/li-etal-2020-molweni/index.md b/content/publication/li-etal-2020-molweni/index.md new file mode 100644 index 0000000..0e2fd85 --- /dev/null +++ b/content/publication/li-etal-2020-molweni/index.md @@ -0,0 +1,35 @@ +--- +title: 'Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension + Dataset with Discourse Structure' +authors: +- Jiaqi Li +- Ming Liu +- Min-Yen Kan +- Zihao Zheng +- Zekun Wang +- Wenqiang Lei +- Ting Liu +- Bing Qin +date: '2020-12-01' +publishDate: '2024-07-11T07:40:56.284002Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 28th International Conference on Computational Linguistics*' +doi: 10.18653/v1/2020.coling-main.238 +abstract: 'Research into the area of multiparty dialog has grown considerably over + recent years. We present the Molweni dataset, a machine reading comprehension (MRC) + dataset with discourse structure built over multiparty dialog. Molweni′s source + samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 + utterances. We annotate 30,066 questions on this corpus, including both answerable + and unanswerable questions. Molweni also uniquely contributes discourse dependency + annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher + et al., 2016) style for all of its multiparty dialogs, contributing large-scale + (78,245 annotated discourse relations) data to bear on the task of multiparty dialog + discourse parsing. Our experiments show that Molweni is a challenging dataset for + current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only + 67.7% F1 on Molweni′s questions, a 20+% significant drop as compared against its + SQuAD 2.0 performance.' +links: +- name: URL + url: https://aclanthology.org/2020.coling-main.238 +--- diff --git a/content/publication/lin-etal-2009-recognizing/cite.bib b/content/publication/lin-etal-2009-recognizing/cite.bib new file mode 100644 index 0000000..5b42002 --- /dev/null +++ b/content/publication/lin-etal-2009-recognizing/cite.bib @@ -0,0 +1,15 @@ +@inproceedings{lin-etal-2009-recognizing, + address = {Singapore}, + author = {Lin, Ziheng and +Kan, Min-Yen and +Ng, Hwee Tou}, + booktitle = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing}, + editor = {Koehn, Philipp and +Mihalcea, Rada}, + month = {August}, + pages = {343--351}, + publisher = {Association for Computational Linguistics}, + title = {Recognizing Implicit Discourse Relations in the Penn Discourse Treebank}, + url = {https://aclanthology.org/D09-1036}, + year = {2009} +} diff --git a/content/publication/lin-etal-2009-recognizing/index.md b/content/publication/lin-etal-2009-recognizing/index.md new file mode 100644 index 0000000..de9f526 --- /dev/null +++ b/content/publication/lin-etal-2009-recognizing/index.md @@ -0,0 +1,16 @@ +--- +title: Recognizing Implicit Discourse Relations in the Penn Discourse Treebank +authors: +- Ziheng Lin +- Min-Yen Kan +- Hwee Tou Ng +date: '2009-08-01' +publishDate: '2024-07-11T07:40:56.548673Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2009 Conference on Empirical Methods in Natural + Language Processing*' +links: +- name: URL + url: https://aclanthology.org/D09-1036 +--- diff --git a/content/publication/lin-etal-2010-extracting/cite.bib b/content/publication/lin-etal-2010-extracting/cite.bib new file mode 100644 index 0000000..bac74a4 --- /dev/null +++ b/content/publication/lin-etal-2010-extracting/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{lin-etal-2010-extracting, + address = {Los Angeles, California, USA}, + author = {Lin, Sein and +Ng, Jun-Ping and +Pradhan, Shreyasee and +Shah, Jatin and +Pietrobon, Ricardo and +Kan, Min-Yen}, + booktitle = {Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents}, + editor = {Dalianis, Hercules and +Hassel, Martin and +Nilsson, Gunnar}, + month = {June}, + pages = {90--95}, + publisher = {Association for Computational Linguistics}, + title = {Extracting Formulaic and Free Text Clinical Research Articles Metadata using Conditional Random Fields}, + url = {https://aclanthology.org/W10-1114}, + year = {2010} +} diff --git a/content/publication/lin-etal-2010-extracting/index.md b/content/publication/lin-etal-2010-extracting/index.md new file mode 100644 index 0000000..670f221 --- /dev/null +++ b/content/publication/lin-etal-2010-extracting/index.md @@ -0,0 +1,20 @@ +--- +title: Extracting Formulaic and Free Text Clinical Research Articles Metadata using + Conditional Random Fields +authors: +- Sein Lin +- Jun-Ping Ng +- Shreyasee Pradhan +- Jatin Shah +- Ricardo Pietrobon +- Min-Yen Kan +date: '2010-06-01' +publishDate: '2024-07-11T07:40:56.481260Z' +publication_types: +- paper-conference +publication: '*Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and + Data Mining of Health Documents*' +links: +- name: URL + url: https://aclanthology.org/W10-1114 +--- diff --git a/content/publication/lin-etal-2011-automatically/cite.bib b/content/publication/lin-etal-2011-automatically/cite.bib new file mode 100644 index 0000000..ea76fc9 --- /dev/null +++ b/content/publication/lin-etal-2011-automatically/cite.bib @@ -0,0 +1,16 @@ +@inproceedings{lin-etal-2011-automatically, + address = {Portland, Oregon, USA}, + author = {Lin, Ziheng and +Ng, Hwee Tou and +Kan, Min-Yen}, + booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, + editor = {Lin, Dekang and +Matsumoto, Yuji and +Mihalcea, Rada}, + month = {June}, + pages = {997--1006}, + publisher = {Association for Computational Linguistics}, + title = {Automatically Evaluating Text Coherence Using Discourse Relations}, + url = {https://aclanthology.org/P11-1100}, + year = {2011} +} diff --git a/content/publication/lin-etal-2011-automatically/index.md b/content/publication/lin-etal-2011-automatically/index.md new file mode 100644 index 0000000..86bb256 --- /dev/null +++ b/content/publication/lin-etal-2011-automatically/index.md @@ -0,0 +1,16 @@ +--- +title: Automatically Evaluating Text Coherence Using Discourse Relations +authors: +- Ziheng Lin +- Hwee Tou Ng +- Min-Yen Kan +date: '2011-06-01' +publishDate: '2024-07-11T07:40:56.475209Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 49th Annual Meeting of the Association for Computational + Linguistics: Human Language Technologies*' +links: +- name: URL + url: https://aclanthology.org/P11-1100 +--- diff --git a/content/publication/lin-etal-2012-combining/cite.bib b/content/publication/lin-etal-2012-combining/cite.bib new file mode 100644 index 0000000..b85149a --- /dev/null +++ b/content/publication/lin-etal-2012-combining/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{lin-etal-2012-combining, + address = {Jeju Island, Korea}, + author = {Lin, Ziheng and +Liu, Chang and +Ng, Hwee Tou and +Kan, Min-Yen}, + booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + editor = {Li, Haizhou and +Lin, Chin-Yew and +Osborne, Miles and +Lee, Gary Geunbae and +Park, Jong C.}, + month = {July}, + pages = {1006--1014}, + publisher = {Association for Computational Linguistics}, + title = {Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization Evaluation}, + url = {https://aclanthology.org/P12-1106}, + year = {2012} +} diff --git a/content/publication/lin-etal-2012-combining/index.md b/content/publication/lin-etal-2012-combining/index.md new file mode 100644 index 0000000..7fa32f1 --- /dev/null +++ b/content/publication/lin-etal-2012-combining/index.md @@ -0,0 +1,18 @@ +--- +title: Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization + Evaluation +authors: +- Ziheng Lin +- Chang Liu +- Hwee Tou Ng +- Min-Yen Kan +date: '2012-07-01' +publishDate: '2024-07-11T07:40:56.456833Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 50th Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +links: +- name: URL + url: https://aclanthology.org/P12-1106 +--- diff --git a/content/publication/lin-kan-2007-timestamped/cite.bib b/content/publication/lin-kan-2007-timestamped/cite.bib new file mode 100644 index 0000000..5cce4c0 --- /dev/null +++ b/content/publication/lin-kan-2007-timestamped/cite.bib @@ -0,0 +1,15 @@ +@inproceedings{lin-kan-2007-timestamped, + address = {Rochester, NY, USA}, + author = {Lin, Ziheng and +Kan, Min-Yen}, + booktitle = {Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms for Natural Language Processing}, + editor = {Biemann, Chris and +Matveeva, Irina and +Mihalcea, Rada and +Radev, Dragomir}, + pages = {25--32}, + publisher = {Association for Computational Linguistics}, + title = {Timestamped Graphs: Evolutionary Models of Text for Multi-Document Summarization}, + url = {https://aclanthology.org/W07-0204}, + year = {2007} +} diff --git a/content/publication/lin-kan-2007-timestamped/index.md b/content/publication/lin-kan-2007-timestamped/index.md new file mode 100644 index 0000000..a96882e --- /dev/null +++ b/content/publication/lin-kan-2007-timestamped/index.md @@ -0,0 +1,15 @@ +--- +title: 'Timestamped Graphs: Evolutionary Models of Text for Multi-Document Summarization' +authors: +- Ziheng Lin +- Min-Yen Kan +date: '2007-01-01' +publishDate: '2024-07-11T07:40:56.573882Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms + for Natural Language Processing*' +links: +- name: URL + url: https://aclanthology.org/W07-0204 +--- diff --git a/content/publication/luong-etal-2010-hybrid/cite.bib b/content/publication/luong-etal-2010-hybrid/cite.bib new file mode 100644 index 0000000..b0f7251 --- /dev/null +++ b/content/publication/luong-etal-2010-hybrid/cite.bib @@ -0,0 +1,15 @@ +@inproceedings{luong-etal-2010-hybrid, + address = {Cambridge, MA}, + author = {Luong, Minh-Thang and +Nakov, Preslav and +Kan, Min-Yen}, + booktitle = {Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing}, + editor = {Li, Hang and +Màrquez, Lluı́s}, + month = {October}, + pages = {148--157}, + publisher = {Association for Computational Linguistics}, + title = {A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages}, + url = {https://aclanthology.org/D10-1015}, + year = {2010} +} diff --git a/content/publication/luong-etal-2010-hybrid/index.md b/content/publication/luong-etal-2010-hybrid/index.md new file mode 100644 index 0000000..4c8f266 --- /dev/null +++ b/content/publication/luong-etal-2010-hybrid/index.md @@ -0,0 +1,17 @@ +--- +title: A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically + Rich Languages +authors: +- Minh-Thang Luong +- Preslav Nakov +- Min-Yen Kan +date: '2010-10-01' +publishDate: '2024-07-11T07:40:56.493708Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2010 Conference on Empirical Methods in Natural + Language Processing*' +links: +- name: URL + url: https://aclanthology.org/D10-1015 +--- diff --git a/content/publication/luong-kan-2010-enhancing/cite.bib b/content/publication/luong-kan-2010-enhancing/cite.bib new file mode 100644 index 0000000..88ad20c --- /dev/null +++ b/content/publication/luong-kan-2010-enhancing/cite.bib @@ -0,0 +1,14 @@ +@inproceedings{luong-kan-2010-enhancing, + address = {Beijing, China}, + author = {Luong, Minh-Thang and +Kan, Min-Yen}, + booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)}, + editor = {Huang, Chu-Ren and +Jurafsky, Dan}, + month = {August}, + pages = {743--751}, + publisher = {Coling 2010 Organizing Committee}, + title = {Enhancing Morphological Alignment for Translating Highly Inflected Languages}, + url = {https://aclanthology.org/C10-1084}, + year = {2010} +} diff --git a/content/publication/luong-kan-2010-enhancing/index.md b/content/publication/luong-kan-2010-enhancing/index.md new file mode 100644 index 0000000..15004dc --- /dev/null +++ b/content/publication/luong-kan-2010-enhancing/index.md @@ -0,0 +1,15 @@ +--- +title: Enhancing Morphological Alignment for Translating Highly Inflected Languages +authors: +- Minh-Thang Luong +- Min-Yen Kan +date: '2010-08-01' +publishDate: '2024-07-11T07:40:56.511952Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 23rd International Conference on Computational Linguistics + (Coling 2010)*' +links: +- name: URL + url: https://aclanthology.org/C10-1084 +--- diff --git a/content/publication/ng-etal-2012-exploiting/cite.bib b/content/publication/ng-etal-2012-exploiting/cite.bib new file mode 100644 index 0000000..3e9142c --- /dev/null +++ b/content/publication/ng-etal-2012-exploiting/cite.bib @@ -0,0 +1,17 @@ +@inproceedings{ng-etal-2012-exploiting, + address = {Mumbai, India}, + author = {Ng, Jun-Ping and +Bysani, Praveen and +Lin, Ziheng and +Kan, Min-Yen and +Tan, Chew-Lim}, + booktitle = {Proceedings of COLING 2012}, + editor = {Kay, Martin and +Boitet, Christian}, + month = {December}, + pages = {2093--2108}, + publisher = {The COLING 2012 Organizing Committee}, + title = {Exploiting Category-Specific Information for Multi-Document Summarization}, + url = {https://aclanthology.org/C12-1128}, + year = {2012} +} diff --git a/content/publication/ng-etal-2012-exploiting/index.md b/content/publication/ng-etal-2012-exploiting/index.md new file mode 100644 index 0000000..465a839 --- /dev/null +++ b/content/publication/ng-etal-2012-exploiting/index.md @@ -0,0 +1,17 @@ +--- +title: Exploiting Category-Specific Information for Multi-Document Summarization +authors: +- Jun-Ping Ng +- Praveen Bysani +- Ziheng Lin +- Min-Yen Kan +- Chew-Lim Tan +date: '2012-12-01' +publishDate: '2024-07-11T07:40:56.463025Z' +publication_types: +- paper-conference +publication: '*Proceedings of COLING 2012*' +links: +- name: URL + url: https://aclanthology.org/C12-1128 +--- diff --git a/content/publication/ng-etal-2013-exploiting/cite.bib b/content/publication/ng-etal-2013-exploiting/cite.bib new file mode 100644 index 0000000..e81b232 --- /dev/null +++ b/content/publication/ng-etal-2013-exploiting/cite.bib @@ -0,0 +1,22 @@ +@inproceedings{ng-etal-2013-exploiting, + address = {Seattle, Washington, USA}, + author = {Ng, Jun-Ping and +Kan, Min-Yen and +Lin, Ziheng and +Feng, Wei and +Chen, Bin and +Su, Jian and +Tan, Chew-Lim}, + booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing}, + editor = {Yarowsky, David and +Baldwin, Timothy and +Korhonen, Anna and +Livescu, Karen and +Bethard, Steven}, + month = {October}, + pages = {12--23}, + publisher = {Association for Computational Linguistics}, + title = {Exploiting Discourse Analysis for Article-Wide Temporal Classification}, + url = {https://aclanthology.org/D13-1002}, + year = {2013} +} diff --git a/content/publication/ng-etal-2013-exploiting/index.md b/content/publication/ng-etal-2013-exploiting/index.md new file mode 100644 index 0000000..62e8307 --- /dev/null +++ b/content/publication/ng-etal-2013-exploiting/index.md @@ -0,0 +1,20 @@ +--- +title: Exploiting Discourse Analysis for Article-Wide Temporal Classification +authors: +- Jun-Ping Ng +- Min-Yen Kan +- Ziheng Lin +- Wei Feng +- Bin Chen +- Jian Su +- Chew-Lim Tan +date: '2013-10-01' +publishDate: '2024-07-11T07:40:56.432328Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2013 Conference on Empirical Methods in Natural + Language Processing*' +links: +- name: URL + url: https://aclanthology.org/D13-1002 +--- diff --git a/content/publication/ng-etal-2014-exploiting/cite.bib b/content/publication/ng-etal-2014-exploiting/cite.bib new file mode 100644 index 0000000..98d6472 --- /dev/null +++ b/content/publication/ng-etal-2014-exploiting/cite.bib @@ -0,0 +1,17 @@ +@inproceedings{ng-etal-2014-exploiting, + address = {Baltimore, Maryland}, + author = {Ng, Jun-Ping and +Chen, Yan and +Kan, Min-Yen and +Li, Zhoujun}, + booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + doi = {10.3115/v1/P14-1087}, + editor = {Toutanova, Kristina and +Wu, Hua}, + month = {June}, + pages = {923--933}, + publisher = {Association for Computational Linguistics}, + title = {Exploiting Timelines to Enhance Multi-document Summarization}, + url = {https://aclanthology.org/P14-1087}, + year = {2014} +} diff --git a/content/publication/ng-etal-2014-exploiting/index.md b/content/publication/ng-etal-2014-exploiting/index.md new file mode 100644 index 0000000..ad3436c --- /dev/null +++ b/content/publication/ng-etal-2014-exploiting/index.md @@ -0,0 +1,18 @@ +--- +title: Exploiting Timelines to Enhance Multi-document Summarization +authors: +- Jun-Ping Ng +- Yan Chen +- Min-Yen Kan +- Zhoujun Li +date: '2014-06-01' +publishDate: '2024-07-11T07:40:56.413715Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 52nd Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +doi: 10.3115/v1/P14-1087 +links: +- name: URL + url: https://aclanthology.org/P14-1087 +--- diff --git a/content/publication/ng-kan-2012-improved/cite.bib b/content/publication/ng-kan-2012-improved/cite.bib new file mode 100644 index 0000000..f6c87c8 --- /dev/null +++ b/content/publication/ng-kan-2012-improved/cite.bib @@ -0,0 +1,14 @@ +@inproceedings{ng-kan-2012-improved, + address = {Mumbai, India}, + author = {Ng, Jun-Ping and +Kan, Min-Yen}, + booktitle = {Proceedings of COLING 2012}, + editor = {Kay, Martin and +Boitet, Christian}, + month = {December}, + pages = {2109--2124}, + publisher = {The COLING 2012 Organizing Committee}, + title = {Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations}, + url = {https://aclanthology.org/C12-1129}, + year = {2012} +} diff --git a/content/publication/ng-kan-2012-improved/index.md b/content/publication/ng-kan-2012-improved/index.md new file mode 100644 index 0000000..1ffbc04 --- /dev/null +++ b/content/publication/ng-kan-2012-improved/index.md @@ -0,0 +1,15 @@ +--- +title: Improved Temporal Relation Classification using Dependency Parses and Selective + Crowdsourced Annotations +authors: +- Jun-Ping Ng +- Min-Yen Kan +date: '2012-12-01' +publishDate: '2024-07-11T07:40:56.469115Z' +publication_types: +- paper-conference +publication: '*Proceedings of COLING 2012*' +links: +- name: URL + url: https://aclanthology.org/C12-1129 +--- diff --git a/content/publication/nguyen-etal-2018-treatment/cite.bib b/content/publication/nguyen-etal-2018-treatment/cite.bib new file mode 100644 index 0000000..6ea923b --- /dev/null +++ b/content/publication/nguyen-etal-2018-treatment/cite.bib @@ -0,0 +1,19 @@ +@inproceedings{nguyen-etal-2018-treatment, + abstract = {With Health 2.0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities. These are helpful platforms for non-professional medical opinions, yet pose risk of being unreliable in quality and insufficient in quantity to cover the wide range of potential drug reactions. Existing approaches which analyze such user-generated content in online forums heavily rely on feature engineering of both documents and users, and often overlook the relationships between posts within a common discussion thread. Inspired by recent advancements, we propose a neural architecture that models the textual content of user-generated documents and user experiences in online communities to predict side effects during treatment. Experimental results show that our proposed architecture outperforms baseline models.}, + address = {Brussels, Belgium}, + author = {Nguyen, Van Hoang and +Sugiyama, Kazunari and +Kan, Min-Yen and +Halder, Kishaloy}, + booktitle = {Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis}, + doi = {10.18653/v1/W18-5602}, + editor = {Lavelli, Alberto and +Minard, Anne-Lyse and +Rinaldi, Fabio}, + month = {October}, + pages = {12--21}, + publisher = {Association for Computational Linguistics}, + title = {Treatment Side Effect Prediction from Online User-generated Content}, + url = {https://aclanthology.org/W18-5602}, + year = {2018} +} diff --git a/content/publication/nguyen-etal-2018-treatment/index.md b/content/publication/nguyen-etal-2018-treatment/index.md new file mode 100644 index 0000000..a9a0399 --- /dev/null +++ b/content/publication/nguyen-etal-2018-treatment/index.md @@ -0,0 +1,29 @@ +--- +title: Treatment Side Effect Prediction from Online User-generated Content +authors: +- Van Hoang Nguyen +- Kazunari Sugiyama +- Min-Yen Kan +- Kishaloy Halder +date: '2018-10-01' +publishDate: '2024-07-11T07:40:56.340987Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Ninth International Workshop on Health Text Mining + and Information Analysis*' +doi: 10.18653/v1/W18-5602 +abstract: With Health 2.0, patients and caregivers increasingly seek information regarding + possible drug side effects during their medical treatments in online health communities. + These are helpful platforms for non-professional medical opinions, yet pose risk + of being unreliable in quality and insufficient in quantity to cover the wide range + of potential drug reactions. Existing approaches which analyze such user-generated + content in online forums heavily rely on feature engineering of both documents and + users, and often overlook the relationships between posts within a common discussion + thread. Inspired by recent advancements, we propose a neural architecture that models + the textual content of user-generated documents and user experiences in online communities + to predict side effects during treatment. Experimental results show that our proposed + architecture outperforms baseline models. +links: +- name: URL + url: https://aclanthology.org/W18-5602 +--- diff --git a/content/publication/pan-etal-2020-semantic/cite.bib b/content/publication/pan-etal-2020-semantic/cite.bib new file mode 100644 index 0000000..11f9426 --- /dev/null +++ b/content/publication/pan-etal-2020-semantic/cite.bib @@ -0,0 +1,21 @@ +@inproceedings{pan-etal-2020-semantic, + abstract = {This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information about the input passage. In order to capture the global structure of the document and facilitate reasoning, we propose a novel framework that first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN). Afterward, we fuse the document-level and graph-level representations to perform joint training of content selection and question decoding. On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance. The code is publicly available at r̆lhttps://github.com/WING-NUS/SG-Deep-Question-Generation.}, + address = {Online}, + author = {Pan, Liangming and +Xie, Yuxi and +Feng, Yansong and +Chua, Tat-Seng and +Kan, Min-Yen}, + booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, + doi = {10.18653/v1/2020.acl-main.135}, + editor = {Jurafsky, Dan and +Chai, Joyce and +Schluter, Natalie and +Tetreault, Joel}, + month = {July}, + pages = {1463--1475}, + publisher = {Association for Computational Linguistics}, + title = {Semantic Graphs for Generating Deep Questions}, + url = {https://aclanthology.org/2020.acl-main.135}, + year = {2020} +} diff --git a/content/publication/pan-etal-2020-semantic/index.md b/content/publication/pan-etal-2020-semantic/index.md new file mode 100644 index 0000000..f3debb2 --- /dev/null +++ b/content/publication/pan-etal-2020-semantic/index.md @@ -0,0 +1,29 @@ +--- +title: Semantic Graphs for Generating Deep Questions +authors: +- Liangming Pan +- Yuxi Xie +- Yansong Feng +- Tat-Seng Chua +- Min-Yen Kan +date: '2020-07-01' +publishDate: '2024-07-11T07:40:56.306034Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 58th Annual Meeting of the Association for Computational + Linguistics*' +doi: 10.18653/v1/2020.acl-main.135 +abstract: This paper proposes the problem of Deep Question Generation (DQG), which + aims to generate complex questions that require reasoning over multiple pieces of + information about the input passage. In order to capture the global structure of + the document and facilitate reasoning, we propose a novel framework that first constructs + a semantic-level graph for the input document and then encodes the semantic graph + by introducing an attention-based GGNN (Att-GGNN). Afterward, we fuse the document-level + and graph-level representations to perform joint training of content selection and + question decoding. On the HotpotQA deep-question centric dataset, our model greatly + improves performance over questions requiring reasoning over multiple facts, leading + to state-of-the-art performance. The code is publicly available at r̆lhttps://github.com/WING-NUS/SG-Deep-Question-Generation. +links: +- name: URL + url: https://aclanthology.org/2020.acl-main.135 +--- diff --git a/content/publication/pan-etal-2021-unsupervised/cite.bib b/content/publication/pan-etal-2021-unsupervised/cite.bib new file mode 100644 index 0000000..09b2f6d --- /dev/null +++ b/content/publication/pan-etal-2021-unsupervised/cite.bib @@ -0,0 +1,26 @@ +@inproceedings{pan-etal-2021-unsupervised, + abstract = {Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. Using only generated training data, we can train a competent multi-hop QA which achieves 61% and 83% of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data. Our codes are publicly available at r̆lhttps://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA.}, + address = {Online}, + author = {Pan, Liangming and +Chen, Wenhu and +Xiong, Wenhan and +Kan, Min-Yen and +Wang, William Yang}, + booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, + doi = {10.18653/v1/2021.naacl-main.469}, + editor = {Toutanova, Kristina and +Rumshisky, Anna and +Zettlemoyer, Luke and +Hakkani-Tur, Dilek and +Beltagy, Iz and +Bethard, Steven and +Cotterell, Ryan and +Chakraborty, Tanmoy and +Zhou, Yichao}, + month = {June}, + pages = {5866--5880}, + publisher = {Association for Computational Linguistics}, + title = {Unsupervised Multi-hop Question Answering by Question Generation}, + url = {https://aclanthology.org/2021.naacl-main.469}, + year = {2021} +} diff --git a/content/publication/pan-etal-2021-unsupervised/index.md b/content/publication/pan-etal-2021-unsupervised/index.md new file mode 100644 index 0000000..886865e --- /dev/null +++ b/content/publication/pan-etal-2021-unsupervised/index.md @@ -0,0 +1,31 @@ +--- +title: Unsupervised Multi-hop Question Answering by Question Generation +authors: +- Liangming Pan +- Wenhu Chen +- Wenhan Xiong +- Min-Yen Kan +- William Yang Wang +date: '2021-06-01' +publishDate: '2024-07-11T07:40:56.233397Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2021 Conference of the North American Chapter of + the Association for Computational Linguistics: Human Language Technologies*' +doi: 10.18653/v1/2021.naacl-main.469 +abstract: Obtaining training data for multi-hop question answering (QA) is time-consuming + and resource-intensive. We explore the possibility to train a well-performed multi-hop + QA model without referencing any human-labeled multi-hop question-answer pairs, + i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised framework that + can generate human-like multi-hop training data from both homogeneous and heterogeneous + data sources. MQA-QG generates questions by first selecting/generating relevant + information from each data source and then integrating the multiple information + to form a multi-hop question. Using only generated training data, we can train a + competent multi-hop QA which achieves 61% and 83% of the supervised learning performance + for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining + the QA system with the generated data would greatly reduce the demand for human-annotated + training data. Our codes are publicly available at r̆lhttps://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA. +links: +- name: URL + url: https://aclanthology.org/2021.naacl-main.469 +--- diff --git a/content/publication/pan-etal-2021-zero/cite.bib b/content/publication/pan-etal-2021-zero/cite.bib new file mode 100644 index 0000000..e6eace6 --- /dev/null +++ b/content/publication/pan-etal-2021-zero/cite.bib @@ -0,0 +1,21 @@ +@inproceedings{pan-etal-2021-zero, + abstract = {Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model′s F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.}, + address = {Online}, + author = {Pan, Liangming and +Chen, Wenhu and +Xiong, Wenhan and +Kan, Min-Yen and +Wang, William Yang}, + booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)}, + doi = {10.18653/v1/2021.acl-short.61}, + editor = {Zong, Chengqing and +Xia, Fei and +Li, Wenjie and +Navigli, Roberto}, + month = {August}, + pages = {476--483}, + publisher = {Association for Computational Linguistics}, + title = {Zero-shot Fact Verification by Claim Generation}, + url = {https://aclanthology.org/2021.acl-short.61}, + year = {2021} +} diff --git a/content/publication/pan-etal-2021-zero/index.md b/content/publication/pan-etal-2021-zero/index.md new file mode 100644 index 0000000..96843b8 --- /dev/null +++ b/content/publication/pan-etal-2021-zero/index.md @@ -0,0 +1,32 @@ +--- +title: Zero-shot Fact Verification by Claim Generation +authors: +- Liangming Pan +- Wenhu Chen +- Wenhan Xiong +- Min-Yen Kan +- William Yang Wang +date: '2021-08-01' +publishDate: '2024-07-11T07:40:56.240829Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 59th Annual Meeting of the Association for Computational + Linguistics and the 11th International Joint Conference on Natural Language Processing + (Volume 2: Short Papers)*' +doi: 10.18653/v1/2021.acl-short.61 +abstract: Neural models for automated fact verification have achieved promising results + thanks to the availability of large, human-annotated datasets. However, for each + new domain that requires fact verification, creating a dataset by manually writing + claims and linking them to their supporting evidence is expensive. We develop QACG, + a framework for training a robust fact verification model by using automatically + generated claims that can be supported, refuted, or unverifiable from evidence from + Wikipedia. QACG generates question-answer pairs from the evidence and then converts + them into different types of claims. Experiments on the FEVER dataset show that + our QACG framework significantly reduces the demand for human-annotated training + data. In a zero-shot scenario, QACG improves a RoBERTa model′s F1 from 50% to 77%, + equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly + available. +links: +- name: URL + url: https://aclanthology.org/2021.acl-short.61 +--- diff --git a/content/publication/prasad-kan-2017-wing/cite.bib b/content/publication/prasad-kan-2017-wing/cite.bib new file mode 100644 index 0000000..0788a1d --- /dev/null +++ b/content/publication/prasad-kan-2017-wing/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{prasad-kan-2017-wing, + abstract = {We describe an end-to-end pipeline processing approach for SemEval 2017′s Task 10 to extract keyphrases and their relations from scientific publications. We jointly identify and classify keyphrases by modeling the subtasks as sequential labeling. Our system utilizes standard, surface-level features along with the adjacent word features, and performs conditional decoding on whole text to extract keyphrases. We focus only on the identification and typing of keyphrases (Subtasks A and B, together referred as extraction), but provide an end-to-end system inclusive of keyphrase relation identification (Subtask C) for completeness. Our top performing configuration achieves an $F_1$ of 0.27 for the end-to-end keyphrase extraction and relation identification scenario on the final test data, and compares on par to other top ranked systems for keyphrase extraction. Our system outperforms other techniques that do not employ global decoding and hence do not account for dependencies between keyphrases. We believe this is crucial for keyphrase classification in the given context of scientific document mining.}, + address = {Vancouver, Canada}, + author = {Prasad, Animesh and +Kan, Min-Yen}, + booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)}, + doi = {10.18653/v1/S17-2170}, + editor = {Bethard, Steven and +Carpuat, Marine and +Apidianaki, Marianna and +Mohammad, Saif M. and +Cer, Daniel and +Jurgens, David}, + month = {August}, + pages = {973--977}, + publisher = {Association for Computational Linguistics}, + title = {WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling}, + url = {https://aclanthology.org/S17-2170}, + year = {2017} +} diff --git a/content/publication/prasad-kan-2017-wing/index.md b/content/publication/prasad-kan-2017-wing/index.md new file mode 100644 index 0000000..a24cf60 --- /dev/null +++ b/content/publication/prasad-kan-2017-wing/index.md @@ -0,0 +1,31 @@ +--- +title: 'WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification + as Joint Sequence Labeling' +authors: +- Animesh Prasad +- Min-Yen Kan +date: '2017-08-01' +publishDate: '2024-07-11T07:40:56.382115Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 11th International Workshop on Semantic Evaluation + (SemEval-2017)*' +doi: 10.18653/v1/S17-2170 +abstract: We describe an end-to-end pipeline processing approach for SemEval 2017′s + Task 10 to extract keyphrases and their relations from scientific publications. + We jointly identify and classify keyphrases by modeling the subtasks as sequential + labeling. Our system utilizes standard, surface-level features along with the adjacent + word features, and performs conditional decoding on whole text to extract keyphrases. + We focus only on the identification and typing of keyphrases (Subtasks A and B, + together referred as extraction), but provide an end-to-end system inclusive of + keyphrase relation identification (Subtask C) for completeness. Our top performing + configuration achieves an $F_1$ of 0.27 for the end-to-end keyphrase extraction + and relation identification scenario on the final test data, and compares on par + to other top ranked systems for keyphrase extraction. Our system outperforms other + techniques that do not employ global decoding and hence do not account for dependencies + between keyphrases. We believe this is crucial for keyphrase classification in the + given context of scientific document mining. +links: +- name: URL + url: https://aclanthology.org/S17-2170 +--- diff --git a/content/publication/prasad-kan-2019-glocal/cite.bib b/content/publication/prasad-kan-2019-glocal/cite.bib new file mode 100644 index 0000000..c25c82b --- /dev/null +++ b/content/publication/prasad-kan-2019-glocal/cite.bib @@ -0,0 +1,17 @@ +@inproceedings{prasad-kan-2019-glocal, + abstract = {Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures. While such methods model nodes′ local pairwise importance, they lack the capability to model global importance relative to other nodes of the graph. This causes such models to miss critical information in tasks where global ranking is a key component for the task, such as in keyphrase extraction. We address this shortcoming by allowing the proper incorporation of global information into the GCN family of models through the use of scaled node weights. In the context of keyphrase extraction, incorporating global random walk scores obtained from TextRank boosts performance significantly. With our proposed method, we achieve state-of-the-art results, bettering a strong baseline by an absolute 2% increase in F1 score.}, + address = {Minneapolis, Minnesota}, + author = {Prasad, Animesh and +Kan, Min-Yen}, + booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)}, + doi = {10.18653/v1/N19-1182}, + editor = {Burstein, Jill and +Doran, Christy and +Solorio, Thamar}, + month = {June}, + pages = {1837--1846}, + publisher = {Association for Computational Linguistics}, + title = {Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction}, + url = {https://aclanthology.org/N19-1182}, + year = {2019} +} diff --git a/content/publication/prasad-kan-2019-glocal/index.md b/content/publication/prasad-kan-2019-glocal/index.md new file mode 100644 index 0000000..3276fc2 --- /dev/null +++ b/content/publication/prasad-kan-2019-glocal/index.md @@ -0,0 +1,29 @@ +--- +title: 'Glocal: Incorporating Global Information in Local Convolution for Keyphrase + Extraction' +authors: +- Animesh Prasad +- Min-Yen Kan +date: '2019-06-01' +publishDate: '2024-07-11T07:40:56.320220Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2019 Conference of the North American Chapter of + the Association for Computational Linguistics: Human Language Technologies, Volume + 1 (Long and Short Papers)*' +doi: 10.18653/v1/N19-1182 +abstract: Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques + that leverage localized convolution filters to perform supervised classification + directly on graphical structures. While such methods model nodes′ local pairwise + importance, they lack the capability to model global importance relative to other + nodes of the graph. This causes such models to miss critical information in tasks + where global ranking is a key component for the task, such as in keyphrase extraction. + We address this shortcoming by allowing the proper incorporation of global information + into the GCN family of models through the use of scaled node weights. In the context + of keyphrase extraction, incorporating global random walk scores obtained from TextRank + boosts performance significantly. With our proposed method, we achieve state-of-the-art + results, bettering a strong baseline by an absolute 2% increase in F1 score. +links: +- name: URL + url: https://aclanthology.org/N19-1182 +--- diff --git a/content/publication/qiu-etal-2004-public/cite.bib b/content/publication/qiu-etal-2004-public/cite.bib new file mode 100644 index 0000000..90b8533 --- /dev/null +++ b/content/publication/qiu-etal-2004-public/cite.bib @@ -0,0 +1,17 @@ +@inproceedings{qiu-etal-2004-public, + address = {Lisbon, Portugal}, + author = {Qiu, Long and +Kan, Min-Yen and +Chua, Tat-Seng}, + booktitle = {Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC′04)}, + editor = {Lino, Maria Teresa and +Xavier, Maria Francisca and +Ferreira, Fátima and +Costa, Rute and +Silva, Raquel}, + month = {May}, + publisher = {European Language Resources Association (ELRA)}, + title = {A Public Reference Implementation of the RAP Anaphora Resolution Algorithm}, + url = {http://www.lrec-conf.org/proceedings/lrec2004/pdf/778.pdf}, + year = {2004} +} diff --git a/content/publication/qiu-etal-2004-public/index.md b/content/publication/qiu-etal-2004-public/index.md new file mode 100644 index 0000000..c449c81 --- /dev/null +++ b/content/publication/qiu-etal-2004-public/index.md @@ -0,0 +1,14 @@ +--- +title: A Public Reference Implementation of the RAP Anaphora Resolution Algorithm +authors: +- Long Qiu +- Min-Yen Kan +- Tat-Seng Chua +date: '2004-05-01' +publishDate: '2024-07-11T07:40:56.604279Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Fourth International Conference on Language Resources + and Evaluation (LREC′04)*' +url_pdf: http://www.lrec-conf.org/proceedings/lrec2004/pdf/778.pdf +--- diff --git a/content/publication/qiu-etal-2006-paraphrase/cite.bib b/content/publication/qiu-etal-2006-paraphrase/cite.bib new file mode 100644 index 0000000..f196f5c --- /dev/null +++ b/content/publication/qiu-etal-2006-paraphrase/cite.bib @@ -0,0 +1,15 @@ +@inproceedings{qiu-etal-2006-paraphrase, + address = {Sydney, Australia}, + author = {Qiu, Long and +Kan, Min-Yen and +Chua, Tat-Seng}, + booktitle = {Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing}, + editor = {Jurafsky, Dan and +Gaussier, Eric}, + month = {July}, + pages = {18--26}, + publisher = {Association for Computational Linguistics}, + title = {Paraphrase Recognition via Dissimilarity Significance Classification}, + url = {https://aclanthology.org/W06-1603}, + year = {2006} +} diff --git a/content/publication/qiu-etal-2006-paraphrase/index.md b/content/publication/qiu-etal-2006-paraphrase/index.md new file mode 100644 index 0000000..60f4240 --- /dev/null +++ b/content/publication/qiu-etal-2006-paraphrase/index.md @@ -0,0 +1,16 @@ +--- +title: Paraphrase Recognition via Dissimilarity Significance Classification +authors: +- Long Qiu +- Min-Yen Kan +- Tat-Seng Chua +date: '2006-07-01' +publishDate: '2024-07-11T07:40:56.598259Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2006 Conference on Empirical Methods in Natural + Language Processing*' +links: +- name: URL + url: https://aclanthology.org/W06-1603 +--- diff --git a/content/publication/qiu-etal-2008-modeling/cite.bib b/content/publication/qiu-etal-2008-modeling/cite.bib new file mode 100644 index 0000000..3c3f25b --- /dev/null +++ b/content/publication/qiu-etal-2008-modeling/cite.bib @@ -0,0 +1,9 @@ +@inproceedings{qiu-etal-2008-modeling, + author = {Qiu, Long and +Kan, Min-Yen and +Chua, Tat-Seng}, + booktitle = {Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I}, + title = {Modeling Context in Scenario Template Creation}, + url = {https://aclanthology.org/I08-1021}, + year = {2008} +} diff --git a/content/publication/qiu-etal-2008-modeling/index.md b/content/publication/qiu-etal-2008-modeling/index.md new file mode 100644 index 0000000..5fca088 --- /dev/null +++ b/content/publication/qiu-etal-2008-modeling/index.md @@ -0,0 +1,16 @@ +--- +title: Modeling Context in Scenario Template Creation +authors: +- Long Qiu +- Min-Yen Kan +- Tat-Seng Chua +date: '2008-01-01' +publishDate: '2024-07-11T07:40:56.567890Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Third International Joint Conference on Natural + Language Processing: Volume-I*' +links: +- name: URL + url: https://aclanthology.org/I08-1021 +--- diff --git a/content/publication/ramesh-kashyap-etal-2021-analyzing/cite.bib b/content/publication/ramesh-kashyap-etal-2021-analyzing/cite.bib new file mode 100644 index 0000000..6d06f58 --- /dev/null +++ b/content/publication/ramesh-kashyap-etal-2021-analyzing/cite.bib @@ -0,0 +1,25 @@ +@inproceedings{ramesh-kashyap-etal-2021-analyzing, + abstract = {The robustness of pretrained language models(PLMs) is generally measured using performance drops on two or more domains. However, we do not yet understand the inherent robustness achieved by contributions from different layers of a PLM. We systematically analyze the robustness of these representations layer by layer from two perspectives. First, we measure the robustness of representations by using domain divergence between two domains. We find that i) Domain variance increases from the lower to the upper layers for vanilla PLMs; ii) Models continuously pretrained on domain-specific data (DAPT)(Gururangan et al., 2020) exhibit more variance than their pretrained PLM counterparts; and that iii) Distilled models (e.g., DistilBERT) also show greater domain variance. Second, we investigate the robustness of representations by analyzing the encoded syntactic and semantic information using diagnostic probes. We find that similar layers have similar amounts of linguistic information for data from an unseen domain.}, + address = {Kyiv, Ukraine}, + author = {Ramesh Kashyap, Abhinav and +Mehnaz, Laiba and +Malik, Bhavitvya and +Waheed, Abdul and +Hazarika, Devamanyu and +Kan, Min-Yen and +Shah, Rajiv Ratn}, + booktitle = {Proceedings of the Second Workshop on Domain Adaptation for NLP}, + editor = {Ben-David, Eyal and +Cohen, Shay and +McDonald, Ryan and +Plank, Barbara and +Reichart, Roi and +Rotman, Guy and +Ziser, Yftah}, + month = {April}, + pages = {222--244}, + publisher = {Association for Computational Linguistics}, + title = {Analyzing the Domain Robustness of Pretrained Language Models, Layer by Layer}, + url = {https://aclanthology.org/2021.adaptnlp-1.23}, + year = {2021} +} diff --git a/content/publication/ramesh-kashyap-etal-2021-analyzing/index.md b/content/publication/ramesh-kashyap-etal-2021-analyzing/index.md new file mode 100644 index 0000000..27ad5fb --- /dev/null +++ b/content/publication/ramesh-kashyap-etal-2021-analyzing/index.md @@ -0,0 +1,32 @@ +--- +title: Analyzing the Domain Robustness of Pretrained Language Models, Layer by Layer +authors: +- Abhinav Ramesh Kashyap +- Laiba Mehnaz +- Bhavitvya Malik +- Abdul Waheed +- Devamanyu Hazarika +- Min-Yen Kan +- Rajiv Ratn Shah +date: '2021-04-01' +publishDate: '2024-07-11T07:40:56.223020Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Second Workshop on Domain Adaptation for NLP*' +abstract: The robustness of pretrained language models(PLMs) is generally measured + using performance drops on two or more domains. However, we do not yet understand + the inherent robustness achieved by contributions from different layers of a PLM. + We systematically analyze the robustness of these representations layer by layer + from two perspectives. First, we measure the robustness of representations by using + domain divergence between two domains. We find that i) Domain variance increases + from the lower to the upper layers for vanilla PLMs; ii) Models continuously pretrained + on domain-specific data (DAPT)(Gururangan et al., 2020) exhibit more variance than + their pretrained PLM counterparts; and that iii) Distilled models (e.g., DistilBERT) + also show greater domain variance. Second, we investigate the robustness of representations + by analyzing the encoded syntactic and semantic information using diagnostic probes. + We find that similar layers have similar amounts of linguistic information for data + from an unseen domain. +links: +- name: URL + url: https://aclanthology.org/2021.adaptnlp-1.23 +--- diff --git a/content/publication/ramesh-kashyap-kan-2020-sciwing/cite.bib b/content/publication/ramesh-kashyap-kan-2020-sciwing/cite.bib new file mode 100644 index 0000000..32e003d --- /dev/null +++ b/content/publication/ramesh-kashyap-kan-2020-sciwing/cite.bib @@ -0,0 +1,25 @@ +@inproceedings{ramesh-kashyap-kan-2020-sciwing, + abstract = {We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from r̆lhttp://sciwing.io and the demos are available at \l̆http://rebrand.ly/sciwing-demo.}, + address = {Online}, + author = {Ramesh Kashyap, Abhinav and +Kan, Min-Yen}, + booktitle = {Proceedings of the First Workshop on Scholarly Document Processing}, + doi = {10.18653/v1/2020.sdp-1.13}, + editor = {Chandrasekaran, Muthu Kumar and +de Waard, Anita and +Feigenblat, Guy and +Freitag, Dayne and +Ghosal, Tirthankar and +Hovy, Eduard and +Knoth, Petr and +Konopnicki, David and +Mayr, Philipp and +Patton, Robert M. and +Shmueli-Scheuer, Michal}, + month = {November}, + pages = {113--120}, + publisher = {Association for Computational Linguistics}, + title = {SciWING-- A Software Toolkit for Scientific Document Processing}, + url = {https://aclanthology.org/2020.sdp-1.13}, + year = {2020} +} diff --git a/content/publication/ramesh-kashyap-kan-2020-sciwing/index.md b/content/publication/ramesh-kashyap-kan-2020-sciwing/index.md new file mode 100644 index 0000000..11f059d --- /dev/null +++ b/content/publication/ramesh-kashyap-kan-2020-sciwing/index.md @@ -0,0 +1,26 @@ +--- +title: SciWING-- A Software Toolkit for Scientific Document Processing +authors: +- Abhinav Ramesh Kashyap +- Min-Yen Kan +date: '2020-11-01' +publishDate: '2024-07-11T07:40:56.255395Z' +publication_types: +- paper-conference +publication: '*Proceedings of the First Workshop on Scholarly Document Processing*' +doi: 10.18653/v1/2020.sdp-1.13 +abstract: We introduce SciWING, an open-source soft-ware toolkit which provides access + to state-of-the-art pre-trained models for scientific document processing (SDP) + tasks, such as citation string parsing, logical structure recovery and citation + intent classification. Compared to other toolkits, SciWING follows a full neural + pipeline and provides a Python inter-face for SDP. When needed, SciWING provides + fine-grained control for rapid experimentation with different models by swapping + and stacking different modules. Transfer learning from general and scientific documents + specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. + SciWING incorporates ready-to-use web and terminal-based applications and demonstrations + to aid adoption and development. The toolkit is available from r̆lhttp://sciwing.io + and the demos are available at l̆http://rebrand.ly/sciwing-demo. +links: +- name: URL + url: https://aclanthology.org/2020.sdp-1.13 +--- diff --git a/content/publication/setiawan-etal-2007-ordering/cite.bib b/content/publication/setiawan-etal-2007-ordering/cite.bib new file mode 100644 index 0000000..a8a0401 --- /dev/null +++ b/content/publication/setiawan-etal-2007-ordering/cite.bib @@ -0,0 +1,15 @@ +@inproceedings{setiawan-etal-2007-ordering, + address = {Prague, Czech Republic}, + author = {Setiawan, Hendra and +Kan, Min-Yen and +Li, Haizhou}, + booktitle = {Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics}, + editor = {Zaenen, Annie and +van den Bosch, Antal}, + month = {June}, + pages = {712--719}, + publisher = {Association for Computational Linguistics}, + title = {Ordering Phrases with Function Words}, + url = {https://aclanthology.org/P07-1090}, + year = {2007} +} diff --git a/content/publication/setiawan-etal-2007-ordering/index.md b/content/publication/setiawan-etal-2007-ordering/index.md new file mode 100644 index 0000000..7942305 --- /dev/null +++ b/content/publication/setiawan-etal-2007-ordering/index.md @@ -0,0 +1,16 @@ +--- +title: Ordering Phrases with Function Words +authors: +- Hendra Setiawan +- Min-Yen Kan +- Haizhou Li +date: '2007-06-01' +publishDate: '2024-07-11T07:40:56.586029Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 45th Annual Meeting of the Association of Computational + Linguistics*' +links: +- name: URL + url: https://aclanthology.org/P07-1090 +--- diff --git a/content/publication/setiawan-etal-2009-topological/cite.bib b/content/publication/setiawan-etal-2009-topological/cite.bib new file mode 100644 index 0000000..ea29b9d --- /dev/null +++ b/content/publication/setiawan-etal-2009-topological/cite.bib @@ -0,0 +1,18 @@ +@inproceedings{setiawan-etal-2009-topological, + address = {Suntec, Singapore}, + author = {Setiawan, Hendra and +Kan, Min-Yen and +Li, Haizhou and +Resnik, Philip}, + booktitle = {Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP}, + editor = {Su, Keh-Yih and +Su, Jian and +Wiebe, Janyce and +Li, Haizhou}, + month = {August}, + pages = {324--332}, + publisher = {Association for Computational Linguistics}, + title = {Topological Ordering of Function Words in Hierarchical Phrase-based Translation}, + url = {https://aclanthology.org/P09-1037}, + year = {2009} +} diff --git a/content/publication/setiawan-etal-2009-topological/index.md b/content/publication/setiawan-etal-2009-topological/index.md new file mode 100644 index 0000000..916bd5f --- /dev/null +++ b/content/publication/setiawan-etal-2009-topological/index.md @@ -0,0 +1,18 @@ +--- +title: Topological Ordering of Function Words in Hierarchical Phrase-based Translation +authors: +- Hendra Setiawan +- Min-Yen Kan +- Haizhou Li +- Philip Resnik +date: '2009-08-01' +publishDate: '2024-07-11T07:40:56.542475Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Joint Conference of the 47th Annual Meeting of the + ACL and the 4th International Joint Conference on Natural Language Processing of + the AFNLP*' +links: +- name: URL + url: https://aclanthology.org/P09-1037 +--- diff --git a/content/publication/si-etal-2019-sentiment/cite.bib b/content/publication/si-etal-2019-sentiment/cite.bib new file mode 100644 index 0000000..140f102 --- /dev/null +++ b/content/publication/si-etal-2019-sentiment/cite.bib @@ -0,0 +1,26 @@ +@inproceedings{si-etal-2019-sentiment, + abstract = {Sentiment ambiguous lexicons refer to words where their polarity depends strongly on con- text. As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data. While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence. We investigate the translation variation in two sentiment scenarios. We perform experiments to study the preservation of sentiment during translation with three different methods that we propose. We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods. We show that NMT can generate both positive- and negative-valent translations of a source sentence, based on a given input sentiment label. Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.}, + address = {Hong Kong, China}, + author = {Si, Chenglei and +Wu, Kui and +Aw, Ai Ti and +Kan, Min-Yen}, + booktitle = {Proceedings of the 6th Workshop on Asian Translation}, + doi = {10.18653/v1/D19-5227}, + editor = {Nakazawa, Toshiaki and +Ding, Chenchen and +Dabre, Raj and +Kunchukuttan, Anoop and +Doi, Nobushige and +Oda, Yusuke and +Bojar, Ondřej and +Parida, Shantipriya and +Goto, Isao and +Mino, Hidaya}, + month = {November}, + pages = {200--206}, + publisher = {Association for Computational Linguistics}, + title = {Sentiment Aware Neural Machine Translation}, + url = {https://aclanthology.org/D19-5227}, + year = {2019} +} diff --git a/content/publication/si-etal-2019-sentiment/index.md b/content/publication/si-etal-2019-sentiment/index.md new file mode 100644 index 0000000..7928181 --- /dev/null +++ b/content/publication/si-etal-2019-sentiment/index.md @@ -0,0 +1,31 @@ +--- +title: Sentiment Aware Neural Machine Translation +authors: +- Chenglei Si +- Kui Wu +- Ai Ti Aw +- Min-Yen Kan +date: '2019-11-01' +publishDate: '2024-07-11T07:40:56.334066Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 6th Workshop on Asian Translation*' +doi: 10.18653/v1/D19-5227 +abstract: Sentiment ambiguous lexicons refer to words where their polarity depends + strongly on con- text. As such, when the context is absent, their translations or + their embedded sentence ends up (incorrectly) being dependent on the training data. + While neural machine translation (NMT) has achieved great progress in recent years, + most systems aim to produce one single correct translation for a given source sentence. + We investigate the translation variation in two sentiment scenarios. We perform + experiments to study the preservation of sentiment during translation with three + different methods that we propose. We conducted tests with both sentiment and non-sentiment + bearing contexts to examine the effectiveness of our methods. We show that NMT can + generate both positive- and negative-valent translations of a source sentence, based + on a given input sentiment label. Empirical evaluations show that our valence-sensitive + embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) + baseline, both in terms of BLEU score and ambiguous word translation accuracy in + test, given non-sentiment bearing contexts. +links: +- name: URL + url: https://aclanthology.org/D19-5227 +--- diff --git a/content/publication/tan-etal-2006-extending/cite.bib b/content/publication/tan-etal-2006-extending/cite.bib new file mode 100644 index 0000000..53df326 --- /dev/null +++ b/content/publication/tan-etal-2006-extending/cite.bib @@ -0,0 +1,9 @@ +@inproceedings{tan-etal-2006-extending, + author = {Tan, Yee Fan and +Kan, Min-Yen and +Cui, Hang}, + booktitle = {Proceedings of the Workshop on Multi-word-expressions in a multilingual context}, + title = {Extending corpus-based identification of light verb constructions using a supervised learning framework}, + url = {https://aclanthology.org/W06-2407}, + year = {2006} +} diff --git a/content/publication/tan-etal-2006-extending/index.md b/content/publication/tan-etal-2006-extending/index.md new file mode 100644 index 0000000..50feaf7 --- /dev/null +++ b/content/publication/tan-etal-2006-extending/index.md @@ -0,0 +1,17 @@ +--- +title: Extending corpus-based identification of light verb constructions using a supervised + learning framework +authors: +- Yee Fan Tan +- Min-Yen Kan +- Hang Cui +date: '2006-01-01' +publishDate: '2024-07-11T07:40:56.592096Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Workshop on Multi-word-expressions in a multilingual + context*' +links: +- name: URL + url: https://aclanthology.org/W06-2407 +--- diff --git a/content/publication/tan-etal-2020-mind/cite.bib b/content/publication/tan-etal-2020-mind/cite.bib new file mode 100644 index 0000000..7e7ad19 --- /dev/null +++ b/content/publication/tan-etal-2020-mind/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{tan-etal-2020-mind, + abstract = {Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English. Although comprehension by human readers is usually unimpaired by non-standard inflections, current NLP systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols. Fine-tuning pretrained NLP models for downstream tasks using our encoding defends against inflectional adversaries while maintaining performance on clean data. Models using BITE generalize better to dialects with non-standard inflections without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers. Since there has been no prior work on quantitatively evaluating vocabulary efficiency, we propose metrics to do so.}, + address = {Online}, + author = {Tan, Samson and +Joty, Shafiq and +Varshney, Lav and +Kan, Min-Yen}, + booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, + doi = {10.18653/v1/2020.emnlp-main.455}, + editor = {Webber, Bonnie and +Cohn, Trevor and +He, Yulan and +Liu, Yang}, + month = {November}, + pages = {5647--5663}, + publisher = {Association for Computational Linguistics}, + title = {Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding}, + url = {https://aclanthology.org/2020.emnlp-main.455}, + year = {2020} +} diff --git a/content/publication/tan-etal-2020-mind/index.md b/content/publication/tan-etal-2020-mind/index.md new file mode 100644 index 0000000..be5a5a1 --- /dev/null +++ b/content/publication/tan-etal-2020-mind/index.md @@ -0,0 +1,32 @@ +--- +title: Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection + Encoding +authors: +- Samson Tan +- Shafiq Joty +- Lav Varshney +- Min-Yen Kan +date: '2020-11-01' +publishDate: '2024-07-11T07:40:56.262379Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 2020 Conference on Empirical Methods in Natural + Language Processing (EMNLP)*' +doi: 10.18653/v1/2020.emnlp-main.455 +abstract: Inflectional variation is a common feature of World Englishes such as Colloquial + Singapore English and African American Vernacular English. Although comprehension + by human readers is usually unimpaired by non-standard inflections, current NLP + systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method + to tokenize English text by reducing inflected words to their base forms before + reinjecting the grammatical information as special symbols. Fine-tuning pretrained + NLP models for downstream tasks using our encoding defends against inflectional + adversaries while maintaining performance on clean data. Models using BITE generalize + better to dialects with non-standard inflections without explicit training and translation + models converge faster when trained with BITE. Finally, we show that our encoding + improves the vocabulary efficiency of popular data-driven subword tokenizers. Since + there has been no prior work on quantitatively evaluating vocabulary efficiency, + we propose metrics to do so. +links: +- name: URL + url: https://aclanthology.org/2020.emnlp-main.455 +--- diff --git a/content/publication/tan-etal-2020-morphin/cite.bib b/content/publication/tan-etal-2020-morphin/cite.bib new file mode 100644 index 0000000..f69310c --- /dev/null +++ b/content/publication/tan-etal-2020-morphin/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{tan-etal-2020-morphin, + abstract = {Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.}, + address = {Online}, + author = {Tan, Samson and +Joty, Shafiq and +Kan, Min-Yen and +Socher, Richard}, + booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, + doi = {10.18653/v1/2020.acl-main.263}, + editor = {Jurafsky, Dan and +Chai, Joyce and +Schluter, Natalie and +Tetreault, Joel}, + month = {July}, + pages = {2920--2935}, + publisher = {Association for Computational Linguistics}, + title = {It′s Morphin′ Time! Combating Linguistic Discrimination with Inflectional Perturbations}, + url = {https://aclanthology.org/2020.acl-main.263}, + year = {2020} +} diff --git a/content/publication/tan-etal-2020-morphin/index.md b/content/publication/tan-etal-2020-morphin/index.md new file mode 100644 index 0000000..dc8f1bd --- /dev/null +++ b/content/publication/tan-etal-2020-morphin/index.md @@ -0,0 +1,26 @@ +--- +title: It′s Morphin′ Time! Combating Linguistic Discrimination with Inflectional Perturbations +authors: +- Samson Tan +- Shafiq Joty +- Min-Yen Kan +- Richard Socher +date: '2020-07-01' +publishDate: '2024-07-11T07:40:56.313557Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 58th Annual Meeting of the Association for Computational + Linguistics*' +doi: 10.18653/v1/2020.acl-main.263 +abstract: Training on only perfect Standard English corpora predisposes pre-trained + neural networks to discriminate against minorities from non-standard linguistic + backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, + etc.). We perturb the inflectional morphology of words to craft plausible and semantically + similar adversarial examples that expose these biases in popular NLP models, e.g., + BERT and Transformer, and show that adversarially fine-tuning them for a single + epoch significantly improves robustness without sacrificing performance on clean + data. +links: +- name: URL + url: https://aclanthology.org/2020.acl-main.263 +--- diff --git a/content/publication/tan-etal-2021-reliability/cite.bib b/content/publication/tan-etal-2021-reliability/cite.bib new file mode 100644 index 0000000..40a512d --- /dev/null +++ b/content/publication/tan-etal-2021-reliability/cite.bib @@ -0,0 +1,22 @@ +@inproceedings{tan-etal-2021-reliability, + abstract = {Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that reliability testing --- with an emphasis on interdisciplinary collaboration --- will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.}, + address = {Online}, + author = {Tan, Samson and +Joty, Shafiq and +Baxter, Kathy and +Taeihagh, Araz and +Bennett, Gregory A. and +Kan, Min-Yen}, + booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)}, + doi = {10.18653/v1/2021.acl-long.321}, + editor = {Zong, Chengqing and +Xia, Fei and +Li, Wenjie and +Navigli, Roberto}, + month = {August}, + pages = {4153--4169}, + publisher = {Association for Computational Linguistics}, + title = {Reliability Testing for Natural Language Processing Systems}, + url = {https://aclanthology.org/2021.acl-long.321}, + year = {2021} +} diff --git a/content/publication/tan-etal-2021-reliability/index.md b/content/publication/tan-etal-2021-reliability/index.md new file mode 100644 index 0000000..d305009 --- /dev/null +++ b/content/publication/tan-etal-2021-reliability/index.md @@ -0,0 +1,30 @@ +--- +title: Reliability Testing for Natural Language Processing Systems +authors: +- Samson Tan +- Shafiq Joty +- Kathy Baxter +- Araz Taeihagh +- Gregory A. Bennett +- Min-Yen Kan +date: '2021-08-01' +publishDate: '2024-07-11T07:40:56.247998Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 59th Annual Meeting of the Association for Computational + Linguistics and the 11th International Joint Conference on Natural Language Processing + (Volume 1: Long Papers)*' +doi: 10.18653/v1/2021.acl-long.321 +abstract: 'Questions of fairness, robustness, and transparency are paramount to address + before deploying NLP systems. Central to these concerns is the question of reliability: + Can NLP systems reliably treat different demographics fairly and function correctly + in diverse and noisy environments? To address this, we argue for the need for reliability + testing and contextualize it among existing work on improving accountability. We + show how adversarial attacks can be reframed for this goal, via a framework for + developing reliability tests. We argue that reliability testing --- with an emphasis + on interdisciplinary collaboration --- will enable rigorous and targeted testing, + and aid in the enactment and enforcement of industry standards.' +links: +- name: URL + url: https://aclanthology.org/2021.acl-long.321 +--- diff --git a/content/publication/wang-etal-2012-tweeting/cite.bib b/content/publication/wang-etal-2012-tweeting/cite.bib new file mode 100644 index 0000000..e2fb329 --- /dev/null +++ b/content/publication/wang-etal-2012-tweeting/cite.bib @@ -0,0 +1,16 @@ +@inproceedings{wang-etal-2012-tweeting, + address = {Montréal, Canada}, + author = {Wang, Aobo and +Chen, Tao and +Kan, Min-Yen}, + booktitle = {Proceedings of the Second Workshop on Language in Social Media}, + editor = {Sood, Sara Owsley and +Nagarajan, Meenakshi and +Gamon, Michael}, + month = {June}, + pages = {46--55}, + publisher = {Association for Computational Linguistics}, + title = {Re-tweeting from a linguistic perspective}, + url = {https://aclanthology.org/W12-2106}, + year = {2012} +} diff --git a/content/publication/wang-etal-2012-tweeting/index.md b/content/publication/wang-etal-2012-tweeting/index.md new file mode 100644 index 0000000..6f38c5a --- /dev/null +++ b/content/publication/wang-etal-2012-tweeting/index.md @@ -0,0 +1,15 @@ +--- +title: Re-tweeting from a linguistic perspective +authors: +- Aobo Wang +- Tao Chen +- Min-Yen Kan +date: '2012-06-01' +publishDate: '2024-07-11T07:40:56.450801Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Second Workshop on Language in Social Media*' +links: +- name: URL + url: https://aclanthology.org/W12-2106 +--- diff --git a/content/publication/wang-etal-2013-chinese/cite.bib b/content/publication/wang-etal-2013-chinese/cite.bib new file mode 100644 index 0000000..ceebda2 --- /dev/null +++ b/content/publication/wang-etal-2013-chinese/cite.bib @@ -0,0 +1,17 @@ +@inproceedings{wang-etal-2013-chinese, + address = {Nagoya, Japan}, + author = {Wang, Aobo and +Kan, Min-Yen and +Andrade, Daniel and +Onishi, Takashi and +Ishikawa, Kai}, + booktitle = {Proceedings of the Sixth International Joint Conference on Natural Language Processing}, + editor = {Mitkov, Ruslan and +Park, Jong C.}, + month = {October}, + pages = {127--135}, + publisher = {Asian Federation of Natural Language Processing}, + title = {Chinese Informal Word Normalization: an Experimental Study}, + url = {https://aclanthology.org/I13-1015}, + year = {2013} +} diff --git a/content/publication/wang-etal-2013-chinese/index.md b/content/publication/wang-etal-2013-chinese/index.md new file mode 100644 index 0000000..54d25e5 --- /dev/null +++ b/content/publication/wang-etal-2013-chinese/index.md @@ -0,0 +1,18 @@ +--- +title: 'Chinese Informal Word Normalization: an Experimental Study' +authors: +- Aobo Wang +- Min-Yen Kan +- Daniel Andrade +- Takashi Onishi +- Kai Ishikawa +date: '2013-10-01' +publishDate: '2024-07-11T07:40:56.426038Z' +publication_types: +- paper-conference +publication: '*Proceedings of the Sixth International Joint Conference on Natural + Language Processing*' +links: +- name: URL + url: https://aclanthology.org/I13-1015 +--- diff --git a/content/publication/wang-kan-2013-mining/cite.bib b/content/publication/wang-kan-2013-mining/cite.bib new file mode 100644 index 0000000..ff8f7ef --- /dev/null +++ b/content/publication/wang-kan-2013-mining/cite.bib @@ -0,0 +1,15 @@ +@inproceedings{wang-kan-2013-mining, + address = {Sofia, Bulgaria}, + author = {Wang, Aobo and +Kan, Min-Yen}, + booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + editor = {Schuetze, Hinrich and +Fung, Pascale and +Poesio, Massimo}, + month = {August}, + pages = {731--741}, + publisher = {Association for Computational Linguistics}, + title = {Mining Informal Language from Chinese Microtext: Joint Word Recognition and Segmentation}, + url = {https://aclanthology.org/P13-1072}, + year = {2013} +} diff --git a/content/publication/wang-kan-2013-mining/index.md b/content/publication/wang-kan-2013-mining/index.md new file mode 100644 index 0000000..06c4f81 --- /dev/null +++ b/content/publication/wang-kan-2013-mining/index.md @@ -0,0 +1,16 @@ +--- +title: 'Mining Informal Language from Chinese Microtext: Joint Word Recognition and + Segmentation' +authors: +- Aobo Wang +- Min-Yen Kan +date: '2013-08-01' +publishDate: '2024-07-11T07:40:56.420009Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 51st Annual Meeting of the Association for Computational + Linguistics (Volume 1: Long Papers)*' +links: +- name: URL + url: https://aclanthology.org/P13-1072 +--- diff --git a/content/publication/ws-2009-2009-text/cite.bib b/content/publication/ws-2009-2009-text/cite.bib new file mode 100644 index 0000000..7ffaec2 --- /dev/null +++ b/content/publication/ws-2009-2009-text/cite.bib @@ -0,0 +1,10 @@ +@proceedings{ws-2009-2009-text, + address = {Suntec City, Singapore}, + editor = {Kan, Min-Yen and +Teufel, Simone}, + month = {August}, + publisher = {Association for Computational Linguistics}, + title = {Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries (NLPIR4DL)}, + url = {https://aclanthology.org/W09-3600}, + year = {2009} +} diff --git a/content/publication/ws-2009-2009-text/index.md b/content/publication/ws-2009-2009-text/index.md new file mode 100644 index 0000000..e0b1ef0 --- /dev/null +++ b/content/publication/ws-2009-2009-text/index.md @@ -0,0 +1,15 @@ +--- +title: Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly + Digital Libraries (NLPIR4DL) +authors: +- Min-Yen Kan +- Simone Teufel +date: '2009-08-01' +publishDate: '2024-07-11T07:40:56.518007Z' +publication_types: +- book +publication: '*Association for Computational Linguistics*' +links: +- name: URL + url: https://aclanthology.org/W09-3600 +--- diff --git a/content/publication/ws-2016-joint/cite.bib b/content/publication/ws-2016-joint/cite.bib new file mode 100644 index 0000000..d4267e0 --- /dev/null +++ b/content/publication/ws-2016-joint/cite.bib @@ -0,0 +1,14 @@ +@proceedings{ws-2016-joint, + editor = {Cabanac, Guillaume and +Chandrasekaran, Muthu Kumar and +Frommholz, Ingo and +Jaidka, Kokil and +Kan, Min-Yen and +Mayr, Philipp and +Wolfram, Dietmar}, + month = {June}, + pages = {1--5}, + title = {Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL)}, + url = {https://aclanthology.org/W16-1500}, + year = {2016} +} diff --git a/content/publication/ws-2016-joint/index.md b/content/publication/ws-2016-joint/index.md new file mode 100644 index 0000000..0e3846c --- /dev/null +++ b/content/publication/ws-2016-joint/index.md @@ -0,0 +1,19 @@ +--- +title: Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval + and Natural Language Processing for Digital Libraries (BIRNDL) +authors: +- Guillaume Cabanac +- Muthu Kumar Chandrasekaran +- Ingo Frommholz +- Kokil Jaidka +- Min-Yen Kan +- Philipp Mayr +- Dietmar Wolfram +date: '2016-06-01' +publishDate: '2024-07-11T07:40:56.395203Z' +publication_types: +- book +links: +- name: URL + url: https://aclanthology.org/W16-1500 +--- diff --git a/content/publication/xie-etal-2020-exploring/cite.bib b/content/publication/xie-etal-2020-exploring/cite.bib new file mode 100644 index 0000000..0099ed3 --- /dev/null +++ b/content/publication/xie-etal-2020-exploring/cite.bib @@ -0,0 +1,20 @@ +@inproceedings{xie-etal-2020-exploring, + abstract = {Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log likelihood of ground-truth questions using teacher forcing. However, this training objective is inconsistent with actual question quality, which is often reflected by certain global properties such as whether the question can be answered by the document. As such, we directly optimize for QG-specific objectives via reinforcement learning to improve question quality. We design three different rewards that target to improve the fluency, relevance, and answerability of generated questions. We conduct both automatic and human evaluations in addition to thorough analysis to explore the effect of each QG-specific reward. We find that optimizing on question-specific rewards generally leads to better performance in automatic evaluation metrics. However, only the rewards that correlate well with human judgement (e.g., relevance) lead to real improvement in question quality. Optimizing for the others, especially answerability, introduces incorrect bias to the model, resulting in poorer question quality. The code is publicly available at r̆lhttps://github.com/YuxiXie/RL-for-Question-Generation.}, + address = {Barcelona, Spain (Online)}, + author = {Xie, Yuxi and +Pan, Liangming and +Wang, Dongzhe and +Kan, Min-Yen and +Feng, Yansong}, + booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, + doi = {10.18653/v1/2020.coling-main.228}, + editor = {Scott, Donia and +Bel, Nuria and +Zong, Chengqing}, + month = {December}, + pages = {2534--2546}, + publisher = {International Committee on Computational Linguistics}, + title = {Exploring Question-Specific Rewards for Generating Deep Questions}, + url = {https://aclanthology.org/2020.coling-main.228}, + year = {2020} +} diff --git a/content/publication/xie-etal-2020-exploring/index.md b/content/publication/xie-etal-2020-exploring/index.md new file mode 100644 index 0000000..d272ffc --- /dev/null +++ b/content/publication/xie-etal-2020-exploring/index.md @@ -0,0 +1,33 @@ +--- +title: Exploring Question-Specific Rewards for Generating Deep Questions +authors: +- Yuxi Xie +- Liangming Pan +- Dongzhe Wang +- Min-Yen Kan +- Yansong Feng +date: '2020-12-01' +publishDate: '2024-07-11T07:40:56.276341Z' +publication_types: +- paper-conference +publication: '*Proceedings of the 28th International Conference on Computational Linguistics*' +doi: 10.18653/v1/2020.coling-main.228 +abstract: Recent question generation (QG) approaches often utilize the sequence-to-sequence + framework (Seq2Seq) to optimize the log likelihood of ground-truth questions using + teacher forcing. However, this training objective is inconsistent with actual question + quality, which is often reflected by certain global properties such as whether the + question can be answered by the document. As such, we directly optimize for QG-specific + objectives via reinforcement learning to improve question quality. We design three + different rewards that target to improve the fluency, relevance, and answerability + of generated questions. We conduct both automatic and human evaluations in addition + to thorough analysis to explore the effect of each QG-specific reward. We find that + optimizing on question-specific rewards generally leads to better performance in + automatic evaluation metrics. However, only the rewards that correlate well with + human judgement (e.g., relevance) lead to real improvement in question quality. + Optimizing for the others, especially answerability, introduces incorrect bias to + the model, resulting in poorer question quality. The code is publicly available + at r̆lhttps://github.com/YuxiXie/RL-for-Question-Generation. +links: +- name: URL + url: https://aclanthology.org/2020.coling-main.228 +--- From 23172ddb68a3bbfb921cde5a76b4c7f71572f848 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Thu, 11 Jul 2024 07:55:09 +0000 Subject: [PATCH 13/22] canonicalize min as an author and link to profile --- content/publication/acl-2017-association-linguistics/index.md | 2 +- content/publication/bhola-etal-2020-retrieving/index.md | 2 +- content/publication/bird-etal-2008-acl/index.md | 2 +- content/publication/bysani-kan-2012-integrating/index.md | 2 +- content/publication/cao-etal-2020-expertise/index.md | 2 +- content/publication/chandrasekaran-kan-2018-countering/index.md | 2 +- content/publication/chen-etal-2015-interactive/index.md | 2 +- content/publication/councill-etal-2008-parscit/index.md | 2 +- content/publication/elmacioglu-etal-2007-psnus/index.md | 2 +- content/publication/gildea-etal-2018-acl/index.md | 2 +- content/publication/halder-etal-2017-modeling/index.md | 2 +- content/publication/halder-etal-2019-predicting/index.md | 2 +- content/publication/hoang-etal-2009-examination/index.md | 2 +- content/publication/hoang-kan-2010-towards/index.md | 2 +- content/publication/jiang-etal-2018-identifying/index.md | 2 +- content/publication/jin-etal-2013-mining/index.md | 2 +- content/publication/kan-2015-keywords/index.md | 2 +- content/publication/kan-etal-1998-linear/index.md | 2 +- content/publication/kan-etal-2001-applying/index.md | 2 +- content/publication/kan-etal-2002-using/index.md | 2 +- content/publication/kan-mckeown-2002-corpus/index.md | 2 +- content/publication/kim-etal-2009-extracting/index.md | 2 +- content/publication/kim-etal-2010-evaluating/index.md | 2 +- content/publication/kim-etal-2010-semeval/index.md | 2 +- content/publication/kim-kan-2009-examining/index.md | 2 +- content/publication/klavans-kan-1998-role-verbs/index.md | 2 +- content/publication/lei-etal-2018-sequicity/index.md | 2 +- content/publication/lei-etal-2020-examining/index.md | 2 +- content/publication/li-etal-2020-molweni/index.md | 2 +- content/publication/lin-etal-2009-recognizing/index.md | 2 +- content/publication/lin-etal-2010-extracting/index.md | 2 +- content/publication/lin-etal-2011-automatically/index.md | 2 +- content/publication/lin-etal-2012-combining/index.md | 2 +- content/publication/lin-kan-2007-timestamped/index.md | 2 +- content/publication/luong-etal-2010-hybrid/index.md | 2 +- content/publication/luong-kan-2010-enhancing/index.md | 2 +- content/publication/ng-etal-2012-exploiting/index.md | 2 +- content/publication/ng-etal-2013-exploiting/index.md | 2 +- content/publication/ng-etal-2014-exploiting/index.md | 2 +- content/publication/ng-kan-2012-improved/index.md | 2 +- content/publication/nguyen-etal-2018-treatment/index.md | 2 +- content/publication/pan-etal-2020-semantic/index.md | 2 +- content/publication/pan-etal-2021-unsupervised/index.md | 2 +- content/publication/pan-etal-2021-zero/index.md | 2 +- content/publication/pan-etal-2023-qacheck/index.md | 2 +- content/publication/prasad-kan-2017-wing/index.md | 2 +- content/publication/prasad-kan-2019-glocal/index.md | 2 +- content/publication/qiu-etal-2004-public/index.md | 2 +- content/publication/qiu-etal-2006-paraphrase/index.md | 2 +- content/publication/qiu-etal-2008-modeling/index.md | 2 +- content/publication/ramesh-kashyap-etal-2021-analyzing/index.md | 2 +- content/publication/ramesh-kashyap-kan-2020-sciwing/index.md | 2 +- content/publication/setiawan-etal-2007-ordering/index.md | 2 +- content/publication/setiawan-etal-2009-topological/index.md | 2 +- content/publication/si-etal-2019-sentiment/index.md | 2 +- content/publication/tan-etal-2006-extending/index.md | 2 +- content/publication/tan-etal-2020-mind/index.md | 2 +- content/publication/tan-etal-2020-morphin/index.md | 2 +- content/publication/tan-etal-2021-reliability/index.md | 2 +- content/publication/wang-etal-2012-tweeting/index.md | 2 +- content/publication/wang-etal-2013-chinese/index.md | 2 +- content/publication/wang-kan-2013-mining/index.md | 2 +- content/publication/ws-2009-2009-text/index.md | 2 +- content/publication/ws-2016-joint/index.md | 2 +- content/publication/xie-etal-2020-exploring/index.md | 2 +- 65 files changed, 65 insertions(+), 65 deletions(-) diff --git a/content/publication/acl-2017-association-linguistics/index.md b/content/publication/acl-2017-association-linguistics/index.md index b03138f..a1e1bd1 100644 --- a/content/publication/acl-2017-association-linguistics/index.md +++ b/content/publication/acl-2017-association-linguistics/index.md @@ -3,7 +3,7 @@ title: 'Proceedings of the 55th Annual Meeting of the Association for Computatio Linguistics (Volume 2: Short Papers)' authors: - Regina Barzilay -- Min-Yen Kan +- min date: '2017-07-01' publishDate: '2024-07-11T07:40:56.389063Z' publication_types: diff --git a/content/publication/bhola-etal-2020-retrieving/index.md b/content/publication/bhola-etal-2020-retrieving/index.md index 7795f4c..010da95 100644 --- a/content/publication/bhola-etal-2020-retrieving/index.md +++ b/content/publication/bhola-etal-2020-retrieving/index.md @@ -5,7 +5,7 @@ authors: - Akshay Bhola - Kishaloy Halder - Animesh Prasad -- Min-Yen Kan +- min date: '2020-12-01' publishDate: '2024-07-11T07:40:56.291153Z' publication_types: diff --git a/content/publication/bird-etal-2008-acl/index.md b/content/publication/bird-etal-2008-acl/index.md index adc4baa..84f8935 100644 --- a/content/publication/bird-etal-2008-acl/index.md +++ b/content/publication/bird-etal-2008-acl/index.md @@ -7,7 +7,7 @@ authors: - Bonnie Dorr - Bryan Gibson - Mark Joseph -- Min-Yen Kan +- min - Dongwon Lee - Brett Powley - Dragomir Radev diff --git a/content/publication/bysani-kan-2012-integrating/index.md b/content/publication/bysani-kan-2012-integrating/index.md index 36ad406..6e2864b 100644 --- a/content/publication/bysani-kan-2012-integrating/index.md +++ b/content/publication/bysani-kan-2012-integrating/index.md @@ -2,7 +2,7 @@ title: Integrating User-Generated Content in the ACL Anthology authors: - Praveen Bysani -- Min-Yen Kan +- min date: '2012-07-01' publishDate: '2024-07-11T07:40:56.444716Z' publication_types: diff --git a/content/publication/cao-etal-2020-expertise/index.md b/content/publication/cao-etal-2020-expertise/index.md index 184bc62..964b048 100644 --- a/content/publication/cao-etal-2020-expertise/index.md +++ b/content/publication/cao-etal-2020-expertise/index.md @@ -5,7 +5,7 @@ authors: - Yixin Cao - Ruihao Shui - Liangming Pan -- Min-Yen Kan +- min - Zhiyuan Liu - Tat-Seng Chua date: '2020-07-01' diff --git a/content/publication/chandrasekaran-kan-2018-countering/index.md b/content/publication/chandrasekaran-kan-2018-countering/index.md index 96700b9..d9529f4 100644 --- a/content/publication/chandrasekaran-kan-2018-countering/index.md +++ b/content/publication/chandrasekaran-kan-2018-countering/index.md @@ -2,7 +2,7 @@ title: Countering Position Bias in Instructor Interventions in MOOC Discussion Forums authors: - Muthu Kumar Chandrasekaran -- Min-Yen Kan +- min date: '2018-07-01' publishDate: '2024-07-11T07:40:56.347855Z' publication_types: diff --git a/content/publication/chen-etal-2015-interactive/index.md b/content/publication/chen-etal-2015-interactive/index.md index 921265b..4f11bff 100644 --- a/content/publication/chen-etal-2015-interactive/index.md +++ b/content/publication/chen-etal-2015-interactive/index.md @@ -5,7 +5,7 @@ authors: - Naijia Zheng - Yue Zhao - Muthu Kumar Chandrasekaran -- Min-Yen Kan +- min date: '2015-07-01' publishDate: '2024-07-11T07:40:56.401303Z' publication_types: diff --git a/content/publication/councill-etal-2008-parscit/index.md b/content/publication/councill-etal-2008-parscit/index.md index 1b346a9..a58d1f1 100644 --- a/content/publication/councill-etal-2008-parscit/index.md +++ b/content/publication/councill-etal-2008-parscit/index.md @@ -3,7 +3,7 @@ title: 'ParsCit: an Open-source CRF Reference String Parsing Package' authors: - Isaac Councill - C. Lee Giles -- Min-Yen Kan +- min date: '2008-05-01' publishDate: '2024-07-11T07:40:56.561520Z' publication_types: diff --git a/content/publication/elmacioglu-etal-2007-psnus/index.md b/content/publication/elmacioglu-etal-2007-psnus/index.md index f61cad8..f967639 100644 --- a/content/publication/elmacioglu-etal-2007-psnus/index.md +++ b/content/publication/elmacioglu-etal-2007-psnus/index.md @@ -4,7 +4,7 @@ authors: - Ergin Elmacioglu - Yee Fan Tan - Su Yan -- Min-Yen Kan +- min - Dongwon Lee date: '2007-06-01' publishDate: '2024-07-11T07:40:56.579914Z' diff --git a/content/publication/gildea-etal-2018-acl/index.md b/content/publication/gildea-etal-2018-acl/index.md index ebeaabc..8184574 100644 --- a/content/publication/gildea-etal-2018-acl/index.md +++ b/content/publication/gildea-etal-2018-acl/index.md @@ -2,7 +2,7 @@ title: 'The ACL Anthology: Current State and Future Directions' authors: - Daniel Gildea -- Min-Yen Kan +- min - Nitin Madnani - Christoph Teichmann - Martı́n Villalba diff --git a/content/publication/halder-etal-2017-modeling/index.md b/content/publication/halder-etal-2017-modeling/index.md index 53f4e0c..7bced06 100644 --- a/content/publication/halder-etal-2017-modeling/index.md +++ b/content/publication/halder-etal-2017-modeling/index.md @@ -4,7 +4,7 @@ title: 'Modeling Temporal Progression of Emotional Status in Mental Health Forum authors: - Kishaloy Halder - Lahari Poddar -- Min-Yen Kan +- min date: '2017-09-01' publishDate: '2024-07-11T07:40:56.375219Z' publication_types: diff --git a/content/publication/halder-etal-2019-predicting/index.md b/content/publication/halder-etal-2019-predicting/index.md index de5cf85..fcdcdc1 100644 --- a/content/publication/halder-etal-2019-predicting/index.md +++ b/content/publication/halder-etal-2019-predicting/index.md @@ -2,7 +2,7 @@ title: 'Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture' authors: - Kishaloy Halder -- Min-Yen Kan +- min - Kazunari Sugiyama date: '2019-06-01' publishDate: '2024-07-11T07:40:56.327069Z' diff --git a/content/publication/hoang-etal-2009-examination/index.md b/content/publication/hoang-etal-2009-examination/index.md index 0fdea75..ff1255a 100644 --- a/content/publication/hoang-etal-2009-examination/index.md +++ b/content/publication/hoang-etal-2009-examination/index.md @@ -3,7 +3,7 @@ title: A re-examination of lexical association measures authors: - Hung Huu Hoang - Su Nam Kim -- Min-Yen Kan +- min date: '2009-08-01' publishDate: '2024-07-11T07:40:56.530100Z' publication_types: diff --git a/content/publication/hoang-kan-2010-towards/index.md b/content/publication/hoang-kan-2010-towards/index.md index 610441a..19aaf6b 100644 --- a/content/publication/hoang-kan-2010-towards/index.md +++ b/content/publication/hoang-kan-2010-towards/index.md @@ -2,7 +2,7 @@ title: Towards Automated Related Work Summarization authors: - Cong Duy Vu Hoang -- Min-Yen Kan +- min date: '2010-08-01' publishDate: '2024-07-11T07:40:56.499873Z' publication_types: diff --git a/content/publication/jiang-etal-2018-identifying/index.md b/content/publication/jiang-etal-2018-identifying/index.md index e7db811..e041afe 100644 --- a/content/publication/jiang-etal-2018-identifying/index.md +++ b/content/publication/jiang-etal-2018-identifying/index.md @@ -3,7 +3,7 @@ title: Identifying Emergent Research Trends by Key Authors and Phrases authors: - Shenhao Jiang - Animesh Prasad -- Min-Yen Kan +- min - Kazunari Sugiyama date: '2018-08-01' publishDate: '2024-07-11T07:40:56.368249Z' diff --git a/content/publication/jin-etal-2013-mining/index.md b/content/publication/jin-etal-2013-mining/index.md index 07ad51a..189a05b 100644 --- a/content/publication/jin-etal-2013-mining/index.md +++ b/content/publication/jin-etal-2013-mining/index.md @@ -2,7 +2,7 @@ title: 'Mining Scientific Terms and their Definitions: A Study of the ACL Anthology' authors: - Yiping Jin -- Min-Yen Kan +- min - Jun-Ping Ng - Xiangnan He date: '2013-10-01' diff --git a/content/publication/kan-2015-keywords/index.md b/content/publication/kan-2015-keywords/index.md index 09138bc..bbe1599 100644 --- a/content/publication/kan-2015-keywords/index.md +++ b/content/publication/kan-2015-keywords/index.md @@ -2,7 +2,7 @@ title: 'Keywords, phrases, clauses and sentences: topicality, indicativeness and informativeness at scales' authors: -- Min-Yen Kan +- min date: '2015-07-01' publishDate: '2024-07-11T07:40:56.407539Z' publication_types: diff --git a/content/publication/kan-etal-1998-linear/index.md b/content/publication/kan-etal-1998-linear/index.md index 161d6bd..f05ae17 100644 --- a/content/publication/kan-etal-1998-linear/index.md +++ b/content/publication/kan-etal-1998-linear/index.md @@ -1,7 +1,7 @@ --- title: Linear Segmentation and Segment Significance authors: -- Min-Yen Kan +- min - Judith L. Klavans - Kathleen R. McKeown date: '1998-01-01' diff --git a/content/publication/kan-etal-2001-applying/index.md b/content/publication/kan-etal-2001-applying/index.md index d7ff2ce..4d4d4e9 100644 --- a/content/publication/kan-etal-2001-applying/index.md +++ b/content/publication/kan-etal-2001-applying/index.md @@ -1,7 +1,7 @@ --- title: Applying Natural Language Generation to Indicative Summarization authors: -- Min-Yen Kan +- min - Kathleen R. McKeown - Judith L. Klavans date: '2001-01-01' diff --git a/content/publication/kan-etal-2002-using/index.md b/content/publication/kan-etal-2002-using/index.md index d463bda..876b82d 100644 --- a/content/publication/kan-etal-2002-using/index.md +++ b/content/publication/kan-etal-2002-using/index.md @@ -1,7 +1,7 @@ --- title: Using the Annotated Bibliography as a Resource for Indicative Summarization authors: -- Min-Yen Kan +- min - Judith L. Klavans - Kathleen R. McKeown date: '2002-05-01' diff --git a/content/publication/kan-mckeown-2002-corpus/index.md b/content/publication/kan-mckeown-2002-corpus/index.md index de4c1b4..ff4ebf0 100644 --- a/content/publication/kan-mckeown-2002-corpus/index.md +++ b/content/publication/kan-mckeown-2002-corpus/index.md @@ -1,7 +1,7 @@ --- title: Corpus-trained Text Generation for Summarization authors: -- Min-Yen Kan +- min - Kathleen R. McKeown date: '2002-07-01' publishDate: '2024-07-11T07:40:56.610197Z' diff --git a/content/publication/kim-etal-2009-extracting/index.md b/content/publication/kim-etal-2009-extracting/index.md index c4f75e6..f4117be 100644 --- a/content/publication/kim-etal-2009-extracting/index.md +++ b/content/publication/kim-etal-2009-extracting/index.md @@ -3,7 +3,7 @@ title: Extracting Domain-Specific Words - A Statistical Approach authors: - Su Nam Kim - Timothy Baldwin -- Min-Yen Kan +- min date: '2009-12-01' publishDate: '2024-07-11T07:40:56.536274Z' publication_types: diff --git a/content/publication/kim-etal-2010-evaluating/index.md b/content/publication/kim-etal-2010-evaluating/index.md index e5a8159..c2b5a5e 100644 --- a/content/publication/kim-etal-2010-evaluating/index.md +++ b/content/publication/kim-etal-2010-evaluating/index.md @@ -3,7 +3,7 @@ title: Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extrac authors: - Su Nam Kim - Timothy Baldwin -- Min-Yen Kan +- min date: '2010-08-01' publishDate: '2024-07-11T07:40:56.505844Z' publication_types: diff --git a/content/publication/kim-etal-2010-semeval/index.md b/content/publication/kim-etal-2010-semeval/index.md index a76c90b..844c860 100644 --- a/content/publication/kim-etal-2010-semeval/index.md +++ b/content/publication/kim-etal-2010-semeval/index.md @@ -3,7 +3,7 @@ title: 'SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Art authors: - Su Nam Kim - Olena Medelyan -- Min-Yen Kan +- min - Timothy Baldwin date: '2010-07-01' publishDate: '2024-07-11T07:40:56.487584Z' diff --git a/content/publication/kim-kan-2009-examining/index.md b/content/publication/kim-kan-2009-examining/index.md index 656dd4a..2131025 100644 --- a/content/publication/kim-kan-2009-examining/index.md +++ b/content/publication/kim-kan-2009-examining/index.md @@ -2,7 +2,7 @@ title: Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles authors: - Su Nam Kim -- Min-Yen Kan +- min date: '2009-08-01' publishDate: '2024-07-11T07:40:56.524050Z' publication_types: diff --git a/content/publication/klavans-kan-1998-role-verbs/index.md b/content/publication/klavans-kan-1998-role-verbs/index.md index a8357e2..9d9ad46 100644 --- a/content/publication/klavans-kan-1998-role-verbs/index.md +++ b/content/publication/klavans-kan-1998-role-verbs/index.md @@ -2,7 +2,7 @@ title: Role of Verbs in Document Analysis authors: - Judith L. Klavans -- Min-Yen Kan +- min date: '1998-08-01' publishDate: '2024-07-11T07:40:56.643091Z' publication_types: diff --git a/content/publication/lei-etal-2018-sequicity/index.md b/content/publication/lei-etal-2018-sequicity/index.md index d00d5c1..faf0c88 100644 --- a/content/publication/lei-etal-2018-sequicity/index.md +++ b/content/publication/lei-etal-2018-sequicity/index.md @@ -4,7 +4,7 @@ title: 'Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequen authors: - Wenqiang Lei - Xisen Jin -- Min-Yen Kan +- min - Zhaochun Ren - Xiangnan He - Dawei Yin diff --git a/content/publication/lei-etal-2020-examining/index.md b/content/publication/lei-etal-2020-examining/index.md index f5d759f..bce6aee 100644 --- a/content/publication/lei-etal-2020-examining/index.md +++ b/content/publication/lei-etal-2020-examining/index.md @@ -6,7 +6,7 @@ authors: - Zhixin Ma - Tian Gan - Wei Lu -- Min-Yen Kan +- min - Tat-Seng Chua date: '2020-11-01' publishDate: '2024-07-11T07:40:56.269384Z' diff --git a/content/publication/li-etal-2020-molweni/index.md b/content/publication/li-etal-2020-molweni/index.md index 0e2fd85..b1c57ae 100644 --- a/content/publication/li-etal-2020-molweni/index.md +++ b/content/publication/li-etal-2020-molweni/index.md @@ -4,7 +4,7 @@ title: 'Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehe authors: - Jiaqi Li - Ming Liu -- Min-Yen Kan +- min - Zihao Zheng - Zekun Wang - Wenqiang Lei diff --git a/content/publication/lin-etal-2009-recognizing/index.md b/content/publication/lin-etal-2009-recognizing/index.md index de9f526..9e7a1f2 100644 --- a/content/publication/lin-etal-2009-recognizing/index.md +++ b/content/publication/lin-etal-2009-recognizing/index.md @@ -2,7 +2,7 @@ title: Recognizing Implicit Discourse Relations in the Penn Discourse Treebank authors: - Ziheng Lin -- Min-Yen Kan +- min - Hwee Tou Ng date: '2009-08-01' publishDate: '2024-07-11T07:40:56.548673Z' diff --git a/content/publication/lin-etal-2010-extracting/index.md b/content/publication/lin-etal-2010-extracting/index.md index 670f221..d9135c2 100644 --- a/content/publication/lin-etal-2010-extracting/index.md +++ b/content/publication/lin-etal-2010-extracting/index.md @@ -7,7 +7,7 @@ authors: - Shreyasee Pradhan - Jatin Shah - Ricardo Pietrobon -- Min-Yen Kan +- min date: '2010-06-01' publishDate: '2024-07-11T07:40:56.481260Z' publication_types: diff --git a/content/publication/lin-etal-2011-automatically/index.md b/content/publication/lin-etal-2011-automatically/index.md index 86bb256..7556325 100644 --- a/content/publication/lin-etal-2011-automatically/index.md +++ b/content/publication/lin-etal-2011-automatically/index.md @@ -3,7 +3,7 @@ title: Automatically Evaluating Text Coherence Using Discourse Relations authors: - Ziheng Lin - Hwee Tou Ng -- Min-Yen Kan +- min date: '2011-06-01' publishDate: '2024-07-11T07:40:56.475209Z' publication_types: diff --git a/content/publication/lin-etal-2012-combining/index.md b/content/publication/lin-etal-2012-combining/index.md index 7fa32f1..52501ad 100644 --- a/content/publication/lin-etal-2012-combining/index.md +++ b/content/publication/lin-etal-2012-combining/index.md @@ -5,7 +5,7 @@ authors: - Ziheng Lin - Chang Liu - Hwee Tou Ng -- Min-Yen Kan +- min date: '2012-07-01' publishDate: '2024-07-11T07:40:56.456833Z' publication_types: diff --git a/content/publication/lin-kan-2007-timestamped/index.md b/content/publication/lin-kan-2007-timestamped/index.md index a96882e..1f18733 100644 --- a/content/publication/lin-kan-2007-timestamped/index.md +++ b/content/publication/lin-kan-2007-timestamped/index.md @@ -2,7 +2,7 @@ title: 'Timestamped Graphs: Evolutionary Models of Text for Multi-Document Summarization' authors: - Ziheng Lin -- Min-Yen Kan +- min date: '2007-01-01' publishDate: '2024-07-11T07:40:56.573882Z' publication_types: diff --git a/content/publication/luong-etal-2010-hybrid/index.md b/content/publication/luong-etal-2010-hybrid/index.md index 4c8f266..09449b7 100644 --- a/content/publication/luong-etal-2010-hybrid/index.md +++ b/content/publication/luong-etal-2010-hybrid/index.md @@ -4,7 +4,7 @@ title: A Hybrid Morpheme-Word Representation for Machine Translation of Morpholo authors: - Minh-Thang Luong - Preslav Nakov -- Min-Yen Kan +- min date: '2010-10-01' publishDate: '2024-07-11T07:40:56.493708Z' publication_types: diff --git a/content/publication/luong-kan-2010-enhancing/index.md b/content/publication/luong-kan-2010-enhancing/index.md index 15004dc..853427b 100644 --- a/content/publication/luong-kan-2010-enhancing/index.md +++ b/content/publication/luong-kan-2010-enhancing/index.md @@ -2,7 +2,7 @@ title: Enhancing Morphological Alignment for Translating Highly Inflected Languages authors: - Minh-Thang Luong -- Min-Yen Kan +- min date: '2010-08-01' publishDate: '2024-07-11T07:40:56.511952Z' publication_types: diff --git a/content/publication/ng-etal-2012-exploiting/index.md b/content/publication/ng-etal-2012-exploiting/index.md index 465a839..0c73807 100644 --- a/content/publication/ng-etal-2012-exploiting/index.md +++ b/content/publication/ng-etal-2012-exploiting/index.md @@ -4,7 +4,7 @@ authors: - Jun-Ping Ng - Praveen Bysani - Ziheng Lin -- Min-Yen Kan +- min - Chew-Lim Tan date: '2012-12-01' publishDate: '2024-07-11T07:40:56.463025Z' diff --git a/content/publication/ng-etal-2013-exploiting/index.md b/content/publication/ng-etal-2013-exploiting/index.md index 62e8307..0b852e8 100644 --- a/content/publication/ng-etal-2013-exploiting/index.md +++ b/content/publication/ng-etal-2013-exploiting/index.md @@ -2,7 +2,7 @@ title: Exploiting Discourse Analysis for Article-Wide Temporal Classification authors: - Jun-Ping Ng -- Min-Yen Kan +- min - Ziheng Lin - Wei Feng - Bin Chen diff --git a/content/publication/ng-etal-2014-exploiting/index.md b/content/publication/ng-etal-2014-exploiting/index.md index ad3436c..1bc7f8b 100644 --- a/content/publication/ng-etal-2014-exploiting/index.md +++ b/content/publication/ng-etal-2014-exploiting/index.md @@ -3,7 +3,7 @@ title: Exploiting Timelines to Enhance Multi-document Summarization authors: - Jun-Ping Ng - Yan Chen -- Min-Yen Kan +- min - Zhoujun Li date: '2014-06-01' publishDate: '2024-07-11T07:40:56.413715Z' diff --git a/content/publication/ng-kan-2012-improved/index.md b/content/publication/ng-kan-2012-improved/index.md index 1ffbc04..b2f1022 100644 --- a/content/publication/ng-kan-2012-improved/index.md +++ b/content/publication/ng-kan-2012-improved/index.md @@ -3,7 +3,7 @@ title: Improved Temporal Relation Classification using Dependency Parses and Sel Crowdsourced Annotations authors: - Jun-Ping Ng -- Min-Yen Kan +- min date: '2012-12-01' publishDate: '2024-07-11T07:40:56.469115Z' publication_types: diff --git a/content/publication/nguyen-etal-2018-treatment/index.md b/content/publication/nguyen-etal-2018-treatment/index.md index a9a0399..3ae6381 100644 --- a/content/publication/nguyen-etal-2018-treatment/index.md +++ b/content/publication/nguyen-etal-2018-treatment/index.md @@ -3,7 +3,7 @@ title: Treatment Side Effect Prediction from Online User-generated Content authors: - Van Hoang Nguyen - Kazunari Sugiyama -- Min-Yen Kan +- min - Kishaloy Halder date: '2018-10-01' publishDate: '2024-07-11T07:40:56.340987Z' diff --git a/content/publication/pan-etal-2020-semantic/index.md b/content/publication/pan-etal-2020-semantic/index.md index f3debb2..efb4442 100644 --- a/content/publication/pan-etal-2020-semantic/index.md +++ b/content/publication/pan-etal-2020-semantic/index.md @@ -5,7 +5,7 @@ authors: - Yuxi Xie - Yansong Feng - Tat-Seng Chua -- Min-Yen Kan +- min date: '2020-07-01' publishDate: '2024-07-11T07:40:56.306034Z' publication_types: diff --git a/content/publication/pan-etal-2021-unsupervised/index.md b/content/publication/pan-etal-2021-unsupervised/index.md index 886865e..fdf1da2 100644 --- a/content/publication/pan-etal-2021-unsupervised/index.md +++ b/content/publication/pan-etal-2021-unsupervised/index.md @@ -4,7 +4,7 @@ authors: - Liangming Pan - Wenhu Chen - Wenhan Xiong -- Min-Yen Kan +- min - William Yang Wang date: '2021-06-01' publishDate: '2024-07-11T07:40:56.233397Z' diff --git a/content/publication/pan-etal-2021-zero/index.md b/content/publication/pan-etal-2021-zero/index.md index 96843b8..d41b59d 100644 --- a/content/publication/pan-etal-2021-zero/index.md +++ b/content/publication/pan-etal-2021-zero/index.md @@ -4,7 +4,7 @@ authors: - Liangming Pan - Wenhu Chen - Wenhan Xiong -- Min-Yen Kan +- min - William Yang Wang date: '2021-08-01' publishDate: '2024-07-11T07:40:56.240829Z' diff --git a/content/publication/pan-etal-2023-qacheck/index.md b/content/publication/pan-etal-2023-qacheck/index.md index 2295d4f..4299039 100644 --- a/content/publication/pan-etal-2023-qacheck/index.md +++ b/content/publication/pan-etal-2023-qacheck/index.md @@ -3,7 +3,7 @@ title: 'QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Check authors: - Liangming Pan - Xinyuan Lu -- Min-Yen Kan +- min - Preslav Nakov date: '2023-12-01' publishDate: '2024-07-06T02:22:24.589500Z' diff --git a/content/publication/prasad-kan-2017-wing/index.md b/content/publication/prasad-kan-2017-wing/index.md index a24cf60..d00130c 100644 --- a/content/publication/prasad-kan-2017-wing/index.md +++ b/content/publication/prasad-kan-2017-wing/index.md @@ -3,7 +3,7 @@ title: 'WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classificatio as Joint Sequence Labeling' authors: - Animesh Prasad -- Min-Yen Kan +- min date: '2017-08-01' publishDate: '2024-07-11T07:40:56.382115Z' publication_types: diff --git a/content/publication/prasad-kan-2019-glocal/index.md b/content/publication/prasad-kan-2019-glocal/index.md index 3276fc2..8a7b98f 100644 --- a/content/publication/prasad-kan-2019-glocal/index.md +++ b/content/publication/prasad-kan-2019-glocal/index.md @@ -3,7 +3,7 @@ title: 'Glocal: Incorporating Global Information in Local Convolution for Keyphr Extraction' authors: - Animesh Prasad -- Min-Yen Kan +- min date: '2019-06-01' publishDate: '2024-07-11T07:40:56.320220Z' publication_types: diff --git a/content/publication/qiu-etal-2004-public/index.md b/content/publication/qiu-etal-2004-public/index.md index c449c81..14ae8dc 100644 --- a/content/publication/qiu-etal-2004-public/index.md +++ b/content/publication/qiu-etal-2004-public/index.md @@ -2,7 +2,7 @@ title: A Public Reference Implementation of the RAP Anaphora Resolution Algorithm authors: - Long Qiu -- Min-Yen Kan +- min - Tat-Seng Chua date: '2004-05-01' publishDate: '2024-07-11T07:40:56.604279Z' diff --git a/content/publication/qiu-etal-2006-paraphrase/index.md b/content/publication/qiu-etal-2006-paraphrase/index.md index 60f4240..9dd4dc0 100644 --- a/content/publication/qiu-etal-2006-paraphrase/index.md +++ b/content/publication/qiu-etal-2006-paraphrase/index.md @@ -2,7 +2,7 @@ title: Paraphrase Recognition via Dissimilarity Significance Classification authors: - Long Qiu -- Min-Yen Kan +- min - Tat-Seng Chua date: '2006-07-01' publishDate: '2024-07-11T07:40:56.598259Z' diff --git a/content/publication/qiu-etal-2008-modeling/index.md b/content/publication/qiu-etal-2008-modeling/index.md index 5fca088..23d4ed0 100644 --- a/content/publication/qiu-etal-2008-modeling/index.md +++ b/content/publication/qiu-etal-2008-modeling/index.md @@ -2,7 +2,7 @@ title: Modeling Context in Scenario Template Creation authors: - Long Qiu -- Min-Yen Kan +- min - Tat-Seng Chua date: '2008-01-01' publishDate: '2024-07-11T07:40:56.567890Z' diff --git a/content/publication/ramesh-kashyap-etal-2021-analyzing/index.md b/content/publication/ramesh-kashyap-etal-2021-analyzing/index.md index 27ad5fb..a9472bc 100644 --- a/content/publication/ramesh-kashyap-etal-2021-analyzing/index.md +++ b/content/publication/ramesh-kashyap-etal-2021-analyzing/index.md @@ -6,7 +6,7 @@ authors: - Bhavitvya Malik - Abdul Waheed - Devamanyu Hazarika -- Min-Yen Kan +- min - Rajiv Ratn Shah date: '2021-04-01' publishDate: '2024-07-11T07:40:56.223020Z' diff --git a/content/publication/ramesh-kashyap-kan-2020-sciwing/index.md b/content/publication/ramesh-kashyap-kan-2020-sciwing/index.md index 11f059d..2d105b5 100644 --- a/content/publication/ramesh-kashyap-kan-2020-sciwing/index.md +++ b/content/publication/ramesh-kashyap-kan-2020-sciwing/index.md @@ -2,7 +2,7 @@ title: SciWING-- A Software Toolkit for Scientific Document Processing authors: - Abhinav Ramesh Kashyap -- Min-Yen Kan +- min date: '2020-11-01' publishDate: '2024-07-11T07:40:56.255395Z' publication_types: diff --git a/content/publication/setiawan-etal-2007-ordering/index.md b/content/publication/setiawan-etal-2007-ordering/index.md index 7942305..2445508 100644 --- a/content/publication/setiawan-etal-2007-ordering/index.md +++ b/content/publication/setiawan-etal-2007-ordering/index.md @@ -2,7 +2,7 @@ title: Ordering Phrases with Function Words authors: - Hendra Setiawan -- Min-Yen Kan +- min - Haizhou Li date: '2007-06-01' publishDate: '2024-07-11T07:40:56.586029Z' diff --git a/content/publication/setiawan-etal-2009-topological/index.md b/content/publication/setiawan-etal-2009-topological/index.md index 916bd5f..0d07225 100644 --- a/content/publication/setiawan-etal-2009-topological/index.md +++ b/content/publication/setiawan-etal-2009-topological/index.md @@ -2,7 +2,7 @@ title: Topological Ordering of Function Words in Hierarchical Phrase-based Translation authors: - Hendra Setiawan -- Min-Yen Kan +- min - Haizhou Li - Philip Resnik date: '2009-08-01' diff --git a/content/publication/si-etal-2019-sentiment/index.md b/content/publication/si-etal-2019-sentiment/index.md index 7928181..e02c077 100644 --- a/content/publication/si-etal-2019-sentiment/index.md +++ b/content/publication/si-etal-2019-sentiment/index.md @@ -4,7 +4,7 @@ authors: - Chenglei Si - Kui Wu - Ai Ti Aw -- Min-Yen Kan +- min date: '2019-11-01' publishDate: '2024-07-11T07:40:56.334066Z' publication_types: diff --git a/content/publication/tan-etal-2006-extending/index.md b/content/publication/tan-etal-2006-extending/index.md index 50feaf7..1c247c3 100644 --- a/content/publication/tan-etal-2006-extending/index.md +++ b/content/publication/tan-etal-2006-extending/index.md @@ -3,7 +3,7 @@ title: Extending corpus-based identification of light verb constructions using a learning framework authors: - Yee Fan Tan -- Min-Yen Kan +- min - Hang Cui date: '2006-01-01' publishDate: '2024-07-11T07:40:56.592096Z' diff --git a/content/publication/tan-etal-2020-mind/index.md b/content/publication/tan-etal-2020-mind/index.md index be5a5a1..b618eba 100644 --- a/content/publication/tan-etal-2020-mind/index.md +++ b/content/publication/tan-etal-2020-mind/index.md @@ -5,7 +5,7 @@ authors: - Samson Tan - Shafiq Joty - Lav Varshney -- Min-Yen Kan +- min date: '2020-11-01' publishDate: '2024-07-11T07:40:56.262379Z' publication_types: diff --git a/content/publication/tan-etal-2020-morphin/index.md b/content/publication/tan-etal-2020-morphin/index.md index dc8f1bd..dec5d5f 100644 --- a/content/publication/tan-etal-2020-morphin/index.md +++ b/content/publication/tan-etal-2020-morphin/index.md @@ -3,7 +3,7 @@ title: It′s Morphin′ Time! Combating Linguistic Discrimination with Inflecti authors: - Samson Tan - Shafiq Joty -- Min-Yen Kan +- min - Richard Socher date: '2020-07-01' publishDate: '2024-07-11T07:40:56.313557Z' diff --git a/content/publication/tan-etal-2021-reliability/index.md b/content/publication/tan-etal-2021-reliability/index.md index d305009..a1aee3e 100644 --- a/content/publication/tan-etal-2021-reliability/index.md +++ b/content/publication/tan-etal-2021-reliability/index.md @@ -6,7 +6,7 @@ authors: - Kathy Baxter - Araz Taeihagh - Gregory A. Bennett -- Min-Yen Kan +- min date: '2021-08-01' publishDate: '2024-07-11T07:40:56.247998Z' publication_types: diff --git a/content/publication/wang-etal-2012-tweeting/index.md b/content/publication/wang-etal-2012-tweeting/index.md index 6f38c5a..60eb307 100644 --- a/content/publication/wang-etal-2012-tweeting/index.md +++ b/content/publication/wang-etal-2012-tweeting/index.md @@ -3,7 +3,7 @@ title: Re-tweeting from a linguistic perspective authors: - Aobo Wang - Tao Chen -- Min-Yen Kan +- min date: '2012-06-01' publishDate: '2024-07-11T07:40:56.450801Z' publication_types: diff --git a/content/publication/wang-etal-2013-chinese/index.md b/content/publication/wang-etal-2013-chinese/index.md index 54d25e5..00a76c1 100644 --- a/content/publication/wang-etal-2013-chinese/index.md +++ b/content/publication/wang-etal-2013-chinese/index.md @@ -2,7 +2,7 @@ title: 'Chinese Informal Word Normalization: an Experimental Study' authors: - Aobo Wang -- Min-Yen Kan +- min - Daniel Andrade - Takashi Onishi - Kai Ishikawa diff --git a/content/publication/wang-kan-2013-mining/index.md b/content/publication/wang-kan-2013-mining/index.md index 06c4f81..1c920f0 100644 --- a/content/publication/wang-kan-2013-mining/index.md +++ b/content/publication/wang-kan-2013-mining/index.md @@ -3,7 +3,7 @@ title: 'Mining Informal Language from Chinese Microtext: Joint Word Recognition Segmentation' authors: - Aobo Wang -- Min-Yen Kan +- min date: '2013-08-01' publishDate: '2024-07-11T07:40:56.420009Z' publication_types: diff --git a/content/publication/ws-2009-2009-text/index.md b/content/publication/ws-2009-2009-text/index.md index e0b1ef0..21ec248 100644 --- a/content/publication/ws-2009-2009-text/index.md +++ b/content/publication/ws-2009-2009-text/index.md @@ -2,7 +2,7 @@ title: Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries (NLPIR4DL) authors: -- Min-Yen Kan +- min - Simone Teufel date: '2009-08-01' publishDate: '2024-07-11T07:40:56.518007Z' diff --git a/content/publication/ws-2016-joint/index.md b/content/publication/ws-2016-joint/index.md index 0e3846c..6bac8d0 100644 --- a/content/publication/ws-2016-joint/index.md +++ b/content/publication/ws-2016-joint/index.md @@ -6,7 +6,7 @@ authors: - Muthu Kumar Chandrasekaran - Ingo Frommholz - Kokil Jaidka -- Min-Yen Kan +- min - Philipp Mayr - Dietmar Wolfram date: '2016-06-01' diff --git a/content/publication/xie-etal-2020-exploring/index.md b/content/publication/xie-etal-2020-exploring/index.md index d272ffc..ff08b3e 100644 --- a/content/publication/xie-etal-2020-exploring/index.md +++ b/content/publication/xie-etal-2020-exploring/index.md @@ -4,7 +4,7 @@ authors: - Yuxi Xie - Liangming Pan - Dongzhe Wang -- Min-Yen Kan +- min - Yansong Feng date: '2020-12-01' publishDate: '2024-07-11T07:40:56.276341Z' From 8c3699b540dc2b59b101073a5faa6e2dfe283055 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Thu, 11 Jul 2024 07:59:43 +0000 Subject: [PATCH 14/22] Fix top link to min's profile --- content/_index.md | 4 +- content/people/index.md | 2 +- publications.bib | 1247 --------------------------------------- 3 files changed, 3 insertions(+), 1250 deletions(-) diff --git a/content/_index.md b/content/_index.md index e8cae81..2c7429c 100644 --- a/content/_index.md +++ b/content/_index.md @@ -12,7 +12,7 @@ sections: image: filename: welcome.jpg text: | - The Web, Information Retrieval / Natural Language Processing Group (WING) explores the research area of applied language processing and information retrieval to the Web and related technologies. Areas of current interest are question answering, scholarly digital libraries, verb similarity, focused crawling, citation parsing and spidering, web page classification and division, text segmentation, and full text analysis. WING is headed by Min (A/P Min-Yen KAN). We are based in the Computational Linguistics Laboratory of the School of Computing at the National University of Singapore. We often work with the Natural Language Processing Group and the Lab for Media Search. We are part of the Media Technologies research group umbrella. + The Web, Information Retrieval / Natural Language Processing Group (WING) explores the research area of applied language processing and information retrieval to the Web and related technologies. Areas of current interest are question answering, scholarly digital libraries, verb similarity, focused crawling, citation parsing and spidering, web page classification and division, text segmentation, and full text analysis. WING is headed by Min (A/P Min-Yen KAN). We are based in the Computational Linguistics Laboratory of the School of Computing at the National University of Singapore. We often work with the Natural Language Processing Group and the Lab for Media Search. We are part of the Media Technologies research group umbrella. - block: collection content: @@ -71,7 +71,7 @@ sections: title: subtitle: text: | - {{% cta cta_link="./people/" cta_text="Meet the team →" %}} + {{% cta cta_link="./people/" %}} design: columns: '1' --- diff --git a/content/people/index.md b/content/people/index.md index 9cdf184..efb63e4 100644 --- a/content/people/index.md +++ b/content/people/index.md @@ -7,7 +7,7 @@ type: landing sections: - block: people content: - title: Meet the Team + # title: Meet the Team # Choose which groups/teams of users to display. # Edit `user_groups` in each user's profile to add them to one or more of these groups. user_groups: diff --git a/publications.bib b/publications.bib index 89f5135..e69de29 100644 --- a/publications.bib +++ b/publications.bib @@ -1,1247 +0,0 @@ - -@inproceedings{ramesh-kashyap-etal-2021-analyzing, - title = "Analyzing the Domain Robustness of Pretrained Language Models, Layer by Layer", - author = "Ramesh Kashyap, Abhinav and - Mehnaz, Laiba and - Malik, Bhavitvya and - Waheed, Abdul and - Hazarika, Devamanyu and - Kan, Min-Yen and - Shah, Rajiv Ratn", - editor = "Ben-David, Eyal and - Cohen, Shay and - McDonald, Ryan and - Plank, Barbara and - Reichart, Roi and - Rotman, Guy and - Ziser, Yftah", - booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP", - month = apr, - year = "2021", - address = "Kyiv, Ukraine", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2021.adaptnlp-1.23", - pages = "222--244", - abstract = "The robustness of pretrained language models(PLMs) is generally measured using performance drops on two or more domains. However, we do not yet understand the inherent robustness achieved by contributions from different layers of a PLM. We systematically analyze the robustness of these representations layer by layer from two perspectives. First, we measure the robustness of representations by using domain divergence between two domains. We find that i) Domain variance increases from the lower to the upper layers for vanilla PLMs; ii) Models continuously pretrained on domain-specific data (DAPT)(Gururangan et al., 2020) exhibit more variance than their pretrained PLM counterparts; and that iii) Distilled models (e.g., DistilBERT) also show greater domain variance. Second, we investigate the robustness of representations by analyzing the encoded syntactic and semantic information using diagnostic probes. We find that similar layers have similar amounts of linguistic information for data from an unseen domain.", -} - - -@inproceedings{pan-etal-2021-unsupervised, - title = "Unsupervised Multi-hop Question Answering by Question Generation", - author = "Pan, Liangming and - Chen, Wenhu and - Xiong, Wenhan and - Kan, Min-Yen and - Wang, William Yang", - editor = "Toutanova, Kristina and - Rumshisky, Anna and - Zettlemoyer, Luke and - Hakkani-Tur, Dilek and - Beltagy, Iz and - Bethard, Steven and - Cotterell, Ryan and - Chakraborty, Tanmoy and - Zhou, Yichao", - booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", - month = jun, - year = "2021", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2021.naacl-main.469", - doi = "10.18653/v1/2021.naacl-main.469", - pages = "5866--5880", - abstract = "Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. Using only generated training data, we can train a competent multi-hop QA which achieves 61{\%} and 83{\%} of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data. Our codes are publicly available at \url{https://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA}.", -} - - -@inproceedings{pan-etal-2021-zero, - title = "Zero-shot Fact Verification by Claim Generation", - author = "Pan, Liangming and - Chen, Wenhu and - Xiong, Wenhan and - Kan, Min-Yen and - Wang, William Yang", - editor = "Zong, Chengqing and - Xia, Fei and - Li, Wenjie and - Navigli, Roberto", - booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)", - month = aug, - year = "2021", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2021.acl-short.61", - doi = "10.18653/v1/2021.acl-short.61", - pages = "476--483", - abstract = "Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model{'}s F1 from 50{\%} to 77{\%}, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.", -} - - -@inproceedings{tan-etal-2021-reliability, - title = "Reliability Testing for Natural Language Processing Systems", - author = "Tan, Samson and - Joty, Shafiq and - Baxter, Kathy and - Taeihagh, Araz and - Bennett, Gregory A. and - Kan, Min-Yen", - editor = "Zong, Chengqing and - Xia, Fei and - Li, Wenjie and - Navigli, Roberto", - booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", - month = aug, - year = "2021", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2021.acl-long.321", - doi = "10.18653/v1/2021.acl-long.321", - pages = "4153--4169", - abstract = "Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that reliability testing {---} with an emphasis on interdisciplinary collaboration {---} will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.", -} - - -@inproceedings{ramesh-kashyap-kan-2020-sciwing, - title = "{S}ci{WING}{--} A Software Toolkit for Scientific Document Processing", - author = "Ramesh Kashyap, Abhinav and - Kan, Min-Yen", - editor = "Chandrasekaran, Muthu Kumar and - de Waard, Anita and - Feigenblat, Guy and - Freitag, Dayne and - Ghosal, Tirthankar and - Hovy, Eduard and - Knoth, Petr and - Konopnicki, David and - Mayr, Philipp and - Patton, Robert M. and - Shmueli-Scheuer, Michal", - booktitle = "Proceedings of the First Workshop on Scholarly Document Processing", - month = nov, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2020.sdp-1.13", - doi = "10.18653/v1/2020.sdp-1.13", - pages = "113--120", - abstract = "We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from \url{http://sciwing.io} and the demos are available at \url{http://rebrand.ly/sciwing-demo}.", -} - - -@inproceedings{tan-etal-2020-mind, - title = "Mind Your Inflections! {I}mproving {NLP} for Non-Standard {E}nglishes with {B}ase-{I}nflection {E}ncoding", - author = "Tan, Samson and - Joty, Shafiq and - Varshney, Lav and - Kan, Min-Yen", - editor = "Webber, Bonnie and - Cohn, Trevor and - He, Yulan and - Liu, Yang", - booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", - month = nov, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2020.emnlp-main.455", - doi = "10.18653/v1/2020.emnlp-main.455", - pages = "5647--5663", - abstract = "Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English. Although comprehension by human readers is usually unimpaired by non-standard inflections, current NLP systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols. Fine-tuning pretrained NLP models for downstream tasks using our encoding defends against inflectional adversaries while maintaining performance on clean data. Models using BITE generalize better to dialects with non-standard inflections without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers. Since there has been no prior work on quantitatively evaluating vocabulary efficiency, we propose metrics to do so.", -} - - -@inproceedings{lei-etal-2020-examining, - title = "Re-examining the Role of Schema Linking in Text-to-{SQL}", - author = "Lei, Wenqiang and - Wang, Weixin and - Ma, Zhixin and - Gan, Tian and - Lu, Wei and - Kan, Min-Yen and - Chua, Tat-Seng", - editor = "Webber, Bonnie and - Cohn, Trevor and - He, Yulan and - Liu, Yang", - booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", - month = nov, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2020.emnlp-main.564", - doi = "10.18653/v1/2020.emnlp-main.564", - pages = "6943--6954", - abstract = "In existing sophisticated text-to-SQL models, schema linking is often considered as a simple, minor component, belying its importance. By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking. We also build a simple BERT-based baseline, called Schema-Linking SQL (SLSQL) to perform a data-driven study. We find when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity. Many remaining errors are attributable to corpus noise. This suggests schema linking is the crux for the current text-to-SQL task. Our analytic studies provide insights on the characteristics of schema linking for future developments of text-to-SQL tasks.", -} - - -@inproceedings{xie-etal-2020-exploring, - title = "Exploring Question-Specific Rewards for Generating Deep Questions", - author = "Xie, Yuxi and - Pan, Liangming and - Wang, Dongzhe and - Kan, Min-Yen and - Feng, Yansong", - editor = "Scott, Donia and - Bel, Nuria and - Zong, Chengqing", - booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", - month = dec, - year = "2020", - address = "Barcelona, Spain (Online)", - publisher = "International Committee on Computational Linguistics", - url = "https://aclanthology.org/2020.coling-main.228", - doi = "10.18653/v1/2020.coling-main.228", - pages = "2534--2546", - abstract = "Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log likelihood of ground-truth questions using teacher forcing. However, this training objective is inconsistent with actual question quality, which is often reflected by certain global properties such as whether the question can be answered by the document. As such, we directly optimize for QG-specific objectives via reinforcement learning to improve question quality. We design three different rewards that target to improve the fluency, relevance, and answerability of generated questions. We conduct both automatic and human evaluations in addition to thorough analysis to explore the effect of each QG-specific reward. We find that optimizing on question-specific rewards generally leads to better performance in automatic evaluation metrics. However, only the rewards that correlate well with human judgement (e.g., relevance) lead to real improvement in question quality. Optimizing for the others, especially answerability, introduces incorrect bias to the model, resulting in poorer question quality. The code is publicly available at \url{https://github.com/YuxiXie/RL-for-Question-Generation}.", -} - - -@inproceedings{li-etal-2020-molweni, - title = "Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure", - author = "Li, Jiaqi and - Liu, Ming and - Kan, Min-Yen and - Zheng, Zihao and - Wang, Zekun and - Lei, Wenqiang and - Liu, Ting and - Qin, Bing", - editor = "Scott, Donia and - Bel, Nuria and - Zong, Chengqing", - booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", - month = dec, - year = "2020", - address = "Barcelona, Spain (Online)", - publisher = "International Committee on Computational Linguistics", - url = "https://aclanthology.org/2020.coling-main.238", - doi = "10.18653/v1/2020.coling-main.238", - pages = "2642--2652", - abstract = "Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni{'}s source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions. Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing. Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7{\%} F1 on Molweni{'}s questions, a 20+{\%} significant drop as compared against its SQuAD 2.0 performance.", -} - - -@inproceedings{bhola-etal-2020-retrieving, - title = "Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework", - author = "Bhola, Akshay and - Halder, Kishaloy and - Prasad, Animesh and - Kan, Min-Yen", - editor = "Scott, Donia and - Bel, Nuria and - Zong, Chengqing", - booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", - month = dec, - year = "2020", - address = "Barcelona, Spain (Online)", - publisher = "International Committee on Computational Linguistics", - url = "https://aclanthology.org/2020.coling-main.513", - doi = "10.18653/v1/2020.coling-main.513", - pages = "5832--5842", - abstract = "We introduce a deep learning model to learn the set of enumerated job skills associated with a job description. In our analysis of a large-scale government job portal mycareersfuture.sg, we observe that as much as 65{\%} of job descriptions miss describing a significant number of relevant skills. Our model addresses this task from the perspective of an extreme multi-label classification (XMLC) problem, where descriptions are the evidence for the binary relevance of thousands of individual skills. Building upon the current state-of-the-art language modeling approaches such as BERT, we show our XMLC method improves on an existing baseline solution by over 9{\%} and 7{\%} absolute improvements in terms of recall and normalized discounted cumulative gain. We further show that our approach effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings by taking into account the structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process. We further show that our approach, to ensure the BERT-XMLC model accounts for structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process, effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings. To facilitate future research and replication of our work, we have made the dataset and the implementation of our model publicly available.", -} - - -@inproceedings{cao-etal-2020-expertise, - title = "Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen", - author = "Cao, Yixin and - Shui, Ruihao and - Pan, Liangming and - Kan, Min-Yen and - Liu, Zhiyuan and - Chua, Tat-Seng", - editor = "Jurafsky, Dan and - Chai, Joyce and - Schluter, Natalie and - Tetreault, Joel", - booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", - month = jul, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2020.acl-main.100", - doi = "10.18653/v1/2020.acl-main.100", - pages = "1061--1071", - abstract = "The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at \url{https://srhthu.github.io/expertise-style-transfer/}.", -} - - -@inproceedings{pan-etal-2020-semantic, - title = "Semantic Graphs for Generating Deep Questions", - author = "Pan, Liangming and - Xie, Yuxi and - Feng, Yansong and - Chua, Tat-Seng and - Kan, Min-Yen", - editor = "Jurafsky, Dan and - Chai, Joyce and - Schluter, Natalie and - Tetreault, Joel", - booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", - month = jul, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2020.acl-main.135", - doi = "10.18653/v1/2020.acl-main.135", - pages = "1463--1475", - abstract = "This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information about the input passage. In order to capture the global structure of the document and facilitate reasoning, we propose a novel framework that first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN). Afterward, we fuse the document-level and graph-level representations to perform joint training of content selection and question decoding. On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance. The code is publicly available at \url{https://github.com/WING-NUS/SG-Deep-Question-Generation}.", -} - - -@inproceedings{tan-etal-2020-morphin, - title = "It{'}s Morphin{'} Time! {C}ombating Linguistic Discrimination with Inflectional Perturbations", - author = "Tan, Samson and - Joty, Shafiq and - Kan, Min-Yen and - Socher, Richard", - editor = "Jurafsky, Dan and - Chai, Joyce and - Schluter, Natalie and - Tetreault, Joel", - booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", - month = jul, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2020.acl-main.263", - doi = "10.18653/v1/2020.acl-main.263", - pages = "2920--2935", - abstract = "Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.", -} - - -@inproceedings{prasad-kan-2019-glocal, - title = "{G}local: Incorporating Global Information in Local Convolution for Keyphrase Extraction", - author = "Prasad, Animesh and - Kan, Min-Yen", - editor = "Burstein, Jill and - Doran, Christy and - Solorio, Thamar", - booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", - month = jun, - year = "2019", - address = "Minneapolis, Minnesota", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/N19-1182", - doi = "10.18653/v1/N19-1182", - pages = "1837--1846", - abstract = "Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures. While such methods model nodes{'} local pairwise importance, they lack the capability to model global importance relative to other nodes of the graph. This causes such models to miss critical information in tasks where global ranking is a key component for the task, such as in keyphrase extraction. We address this shortcoming by allowing the proper incorporation of global information into the GCN family of models through the use of scaled node weights. In the context of keyphrase extraction, incorporating global random walk scores obtained from TextRank boosts performance significantly. With our proposed method, we achieve state-of-the-art results, bettering a strong baseline by an absolute 2{\%} increase in F1 score.", -} - - -@inproceedings{halder-etal-2019-predicting, - title = "Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture", - author = "Halder, Kishaloy and - Kan, Min-Yen and - Sugiyama, Kazunari", - editor = "Burstein, Jill and - Doran, Christy and - Solorio, Thamar", - booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", - month = jun, - year = "2019", - address = "Minneapolis, Minnesota", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/N19-1318", - doi = "10.18653/v1/N19-1318", - pages = "3148--3157", - abstract = "Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or by sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread to help users comprehend long running discussion threads, which often contain repetitive or irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.", -} - - -@inproceedings{si-etal-2019-sentiment, - title = "Sentiment Aware Neural Machine Translation", - author = "Si, Chenglei and - Wu, Kui and - Aw, Ai Ti and - Kan, Min-Yen", - editor = "Nakazawa, Toshiaki and - Ding, Chenchen and - Dabre, Raj and - Kunchukuttan, Anoop and - Doi, Nobushige and - Oda, Yusuke and - Bojar, Ond{\v{r}}ej and - Parida, Shantipriya and - Goto, Isao and - Mino, Hidaya", - booktitle = "Proceedings of the 6th Workshop on Asian Translation", - month = nov, - year = "2019", - address = "Hong Kong, China", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/D19-5227", - doi = "10.18653/v1/D19-5227", - pages = "200--206", - abstract = "Sentiment ambiguous lexicons refer to words where their polarity depends strongly on con- text. As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data. While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence. We investigate the translation variation in two sentiment scenarios. We perform experiments to study the preservation of sentiment during translation with three different methods that we propose. We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods. We show that NMT can generate both positive- and negative-valent translations of a source sentence, based on a given input sentiment label. Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.", -} - - -@inproceedings{nguyen-etal-2018-treatment, - title = "Treatment Side Effect Prediction from Online User-generated Content", - author = "Nguyen, Van Hoang and - Sugiyama, Kazunari and - Kan, Min-Yen and - Halder, Kishaloy", - editor = "Lavelli, Alberto and - Minard, Anne-Lyse and - Rinaldi, Fabio", - booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis", - month = oct, - year = "2018", - address = "Brussels, Belgium", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W18-5602", - doi = "10.18653/v1/W18-5602", - pages = "12--21", - abstract = "With Health 2.0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities. These are helpful platforms for non-professional medical opinions, yet pose risk of being unreliable in quality and insufficient in quantity to cover the wide range of potential drug reactions. Existing approaches which analyze such user-generated content in online forums heavily rely on feature engineering of both documents and users, and often overlook the relationships between posts within a common discussion thread. Inspired by recent advancements, we propose a neural architecture that models the textual content of user-generated documents and user experiences in online communities to predict side effects during treatment. Experimental results show that our proposed architecture outperforms baseline models.", -} - - -@inproceedings{chandrasekaran-kan-2018-countering, - title = "Countering Position Bias in Instructor Interventions in {MOOC} Discussion Forums", - author = "Chandrasekaran, Muthu Kumar and - Kan, Min-Yen", - editor = "Tseng, Yuen-Hsien and - Chen, Hsin-Hsi and - Ng, Vincent and - Komachi, Mamoru", - booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications", - month = jul, - year = "2018", - address = "Melbourne, Australia", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W18-3720", - doi = "10.18653/v1/W18-3720", - pages = "135--142", - abstract = "We systematically confirm that instructors are strongly influenced by the user interface presentation of Massive Online Open Course (MOOC) discussion forums. In a large scale dataset, we conclusively show that instructor interventions exhibit strong position bias, as measured by the position where the thread appeared on the user interface at the time of intervention. We measure and remove this bias, enabling unbiased statistical modelling and evaluation. We show that our de-biased classifier improves predicting interventions over the state-of-the-art on courses with sufficient number of interventions by 8.2{\%} in F1 and 24.4{\%} in recall on average.", -} - - -@inproceedings{gildea-etal-2018-acl, - title = "The {ACL} {A}nthology: Current State and Future Directions", - author = "Gildea, Daniel and - Kan, Min-Yen and - Madnani, Nitin and - Teichmann, Christoph and - Villalba, Mart{\'\i}n", - editor = "Park, Eunjeong L. and - Hagiwara, Masato and - Milajevs, Dmitrijs and - Tan, Liling", - booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})", - month = jul, - year = "2018", - address = "Melbourne, Australia", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W18-2504", - doi = "10.18653/v1/W18-2504", - pages = "23--28", - abstract = "The Association of Computational Linguistic{'}s Anthology is the open source archive, and the main source for computational linguistics and natural language processing{'}s scientific literature. The ACL Anthology is currently maintained exclusively by community volunteers and has to be available and up-to-date at all times. We first discuss the current, open source approach used to achieve this, and then discuss how the planned use of Docker images will improve the Anthology{'}s long-term stability. This change will make it easier for researchers to utilize Anthology data for experimentation. We believe the ACL community can directly benefit from the extension-friendly architecture of the Anthology. We end by issuing an open challenge of reviewer matching we encourage the community to rally towards.", -} - - -@inproceedings{lei-etal-2018-sequicity, - title = "{S}equicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures", - author = "Lei, Wenqiang and - Jin, Xisen and - Kan, Min-Yen and - Ren, Zhaochun and - He, Xiangnan and - Yin, Dawei", - editor = "Gurevych, Iryna and - Miyao, Yusuke", - booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = jul, - year = "2018", - address = "Melbourne, Australia", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P18-1133", - doi = "10.18653/v1/P18-1133", - pages = "1437--1447", - abstract = "Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.", -} - - -@inproceedings{jiang-etal-2018-identifying, - title = "Identifying Emergent Research Trends by Key Authors and Phrases", - author = "Jiang, Shenhao and - Prasad, Animesh and - Kan, Min-Yen and - Sugiyama, Kazunari", - editor = "Bender, Emily M. and - Derczynski, Leon and - Isabelle, Pierre", - booktitle = "Proceedings of the 27th International Conference on Computational Linguistics", - month = aug, - year = "2018", - address = "Santa Fe, New Mexico, USA", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/C18-1022", - pages = "259--269", - abstract = "Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield insight on developing topics. We propose an embedded trend detection framework for this task which incorporates our bijunctive hypothesis that important phrases are written by important authors within a field and vice versa. By ranking both author and phrase information in a multigraph, our method jointly determines key phrases and authoritative authors. We represent this intermediate output as phrasal embeddings, and feed this to a recurrent neural network (RNN) to compute trend scores that identify research trends. Over two large datasets of scientific articles, we demonstrate that our approach successfully detects past trends from the field, outperforming baselines based solely on text centrality or citation.", -} - - -@inproceedings{halder-etal-2017-modeling, - title = "Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach", - author = "Halder, Kishaloy and - Poddar, Lahari and - Kan, Min-Yen", - editor = "Balahur, Alexandra and - Mohammad, Saif M. and - van der Goot, Erik", - booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis", - month = sep, - year = "2017", - address = "Copenhagen, Denmark", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W17-5217", - doi = "10.18653/v1/W17-5217", - pages = "127--135", - abstract = "Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual{'}s posts. We analyze a real-world dataset from the Mental Health section of HealthBoards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely. We study the problem of predicting a patient{'}s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.", -} - - -@inproceedings{prasad-kan-2017-wing, - title = "{WING}-{NUS} at {S}em{E}val-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling", - author = "Prasad, Animesh and - Kan, Min-Yen", - editor = "Bethard, Steven and - Carpuat, Marine and - Apidianaki, Marianna and - Mohammad, Saif M. and - Cer, Daniel and - Jurgens, David", - booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)", - month = aug, - year = "2017", - address = "Vancouver, Canada", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/S17-2170", - doi = "10.18653/v1/S17-2170", - pages = "973--977", - abstract = "We describe an end-to-end pipeline processing approach for SemEval 2017{'}s Task 10 to extract keyphrases and their relations from scientific publications. We jointly identify and classify keyphrases by modeling the subtasks as sequential labeling. Our system utilizes standard, surface-level features along with the adjacent word features, and performs conditional decoding on whole text to extract keyphrases. We focus only on the identification and typing of keyphrases (Subtasks A and B, together referred as extraction), but provide an end-to-end system inclusive of keyphrase relation identification (Subtask C) for completeness. Our top performing configuration achieves an $F_1$ of 0.27 for the end-to-end keyphrase extraction and relation identification scenario on the final test data, and compares on par to other top ranked systems for keyphrase extraction. Our system outperforms other techniques that do not employ global decoding and hence do not account for dependencies between keyphrases. We believe this is crucial for keyphrase classification in the given context of scientific document mining.", -} - - -@proceedings{acl-2017-association-linguistics, - title = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", - editor = "Barzilay, Regina and - Kan, Min-Yen", - month = jul, - year = "2017", - address = "Vancouver, Canada", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P17-2000", - doi = "10.18653/v1/P17-2", -} - - -@proceedings{ws-2016-joint, - title = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})", - editor = "Cabanac, Guillaume and - Chandrasekaran, Muthu Kumar and - Frommholz, Ingo and - Jaidka, Kokil and - Kan, Min-Yen and - Mayr, Philipp and - Wolfram, Dietmar", - month = jun, - year = "2016", - url = "https://aclanthology.org/W16-1500", - pages = "1--5", -} - - -@inproceedings{chen-etal-2015-interactive, - title = "Interactive Second Language Learning from News Websites", - author = "Chen, Tao and - Zheng, Naijia and - Zhao, Yue and - Chandrasekaran, Muthu Kumar and - Kan, Min-Yen", - editor = "Chen, Hsin-Hsi and - Tseng, Yuen-Hsien and - Matsumoto, Yuji and - Wong, Lung Hsiang", - booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications", - month = jul, - year = "2015", - address = "Beijing, China", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W15-4406", - doi = "10.18653/v1/W15-4406", - pages = "34--42", -} - - -@inproceedings{kan-2015-keywords, - title = "Keywords, phrases, clauses and sentences: topicality, indicativeness and informativeness at scales", - author = "Kan, Min-Yen", - editor = "Gollapalli, Sujatha Das and - Caragea, Cornelia and - Li, Xiaoli and - Giles, C. Lee", - booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction", - month = jul, - year = "2015", - address = "Beijing, China", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W15-3601", - doi = "10.18653/v1/W15-3601", - pages = "1", -} - - -@inproceedings{ng-etal-2014-exploiting, - title = "Exploiting Timelines to Enhance Multi-document Summarization", - author = "Ng, Jun-Ping and - Chen, Yan and - Kan, Min-Yen and - Li, Zhoujun", - editor = "Toutanova, Kristina and - Wu, Hua", - booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = jun, - year = "2014", - address = "Baltimore, Maryland", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P14-1087", - doi = "10.3115/v1/P14-1087", - pages = "923--933", -} - - -@inproceedings{wang-kan-2013-mining, - title = "Mining Informal Language from {C}hinese Microtext: Joint Word Recognition and Segmentation", - author = "Wang, Aobo and - Kan, Min-Yen", - editor = "Schuetze, Hinrich and - Fung, Pascale and - Poesio, Massimo", - booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = aug, - year = "2013", - address = "Sofia, Bulgaria", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P13-1072", - pages = "731--741", -} - - -@inproceedings{wang-etal-2013-chinese, - title = "{C}hinese Informal Word Normalization: an Experimental Study", - author = "Wang, Aobo and - Kan, Min-Yen and - Andrade, Daniel and - Onishi, Takashi and - Ishikawa, Kai", - editor = "Mitkov, Ruslan and - Park, Jong C.", - booktitle = "Proceedings of the Sixth International Joint Conference on Natural Language Processing", - month = oct, - year = "2013", - address = "Nagoya, Japan", - publisher = "Asian Federation of Natural Language Processing", - url = "https://aclanthology.org/I13-1015", - pages = "127--135", -} - - -@inproceedings{ng-etal-2013-exploiting, - title = "Exploiting Discourse Analysis for Article-Wide Temporal Classification", - author = "Ng, Jun-Ping and - Kan, Min-Yen and - Lin, Ziheng and - Feng, Wei and - Chen, Bin and - Su, Jian and - Tan, Chew-Lim", - editor = "Yarowsky, David and - Baldwin, Timothy and - Korhonen, Anna and - Livescu, Karen and - Bethard, Steven", - booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", - month = oct, - year = "2013", - address = "Seattle, Washington, USA", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/D13-1002", - pages = "12--23", -} - - -@inproceedings{jin-etal-2013-mining, - title = "Mining Scientific Terms and their Definitions: A Study of the {ACL} {A}nthology", - author = "Jin, Yiping and - Kan, Min-Yen and - Ng, Jun-Ping and - He, Xiangnan", - editor = "Yarowsky, David and - Baldwin, Timothy and - Korhonen, Anna and - Livescu, Karen and - Bethard, Steven", - booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", - month = oct, - year = "2013", - address = "Seattle, Washington, USA", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/D13-1073", - pages = "780--790", -} - - -@inproceedings{bysani-kan-2012-integrating, - title = "Integrating User-Generated Content in the {ACL} {A}nthology", - author = "Bysani, Praveen and - Kan, Min-Yen", - editor = "Banchs, Rafael E.", - booktitle = "Proceedings of the {ACL}-2012 Special Workshop on Rediscovering 50 Years of Discoveries", - month = jul, - year = "2012", - address = "Jeju Island, Korea", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W12-3209", - pages = "83--87", -} - - -@inproceedings{wang-etal-2012-tweeting, - title = "Re-tweeting from a linguistic perspective", - author = "Wang, Aobo and - Chen, Tao and - Kan, Min-Yen", - editor = "Sood, Sara Owsley and - Nagarajan, Meenakshi and - Gamon, Michael", - booktitle = "Proceedings of the Second Workshop on Language in Social Media", - month = jun, - year = "2012", - address = "Montr{\'e}al, Canada", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W12-2106", - pages = "46--55", -} - - -@inproceedings{lin-etal-2012-combining, - title = "Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization Evaluation", - author = "Lin, Ziheng and - Liu, Chang and - Ng, Hwee Tou and - Kan, Min-Yen", - editor = "Li, Haizhou and - Lin, Chin-Yew and - Osborne, Miles and - Lee, Gary Geunbae and - Park, Jong C.", - booktitle = "Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = jul, - year = "2012", - address = "Jeju Island, Korea", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P12-1106", - pages = "1006--1014", -} - - -@inproceedings{ng-etal-2012-exploiting, - title = "Exploiting Category-Specific Information for Multi-Document Summarization", - author = "Ng, Jun-Ping and - Bysani, Praveen and - Lin, Ziheng and - Kan, Min-Yen and - Tan, Chew-Lim", - editor = "Kay, Martin and - Boitet, Christian", - booktitle = "Proceedings of {COLING} 2012", - month = dec, - year = "2012", - address = "Mumbai, India", - publisher = "The COLING 2012 Organizing Committee", - url = "https://aclanthology.org/C12-1128", - pages = "2093--2108", -} - - -@inproceedings{ng-kan-2012-improved, - title = "Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations", - author = "Ng, Jun-Ping and - Kan, Min-Yen", - editor = "Kay, Martin and - Boitet, Christian", - booktitle = "Proceedings of {COLING} 2012", - month = dec, - year = "2012", - address = "Mumbai, India", - publisher = "The COLING 2012 Organizing Committee", - url = "https://aclanthology.org/C12-1129", - pages = "2109--2124", -} - - -@inproceedings{lin-etal-2011-automatically, - title = "Automatically Evaluating Text Coherence Using Discourse Relations", - author = "Lin, Ziheng and - Ng, Hwee Tou and - Kan, Min-Yen", - editor = "Lin, Dekang and - Matsumoto, Yuji and - Mihalcea, Rada", - booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies", - month = jun, - year = "2011", - address = "Portland, Oregon, USA", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P11-1100", - pages = "997--1006", -} - - -@inproceedings{lin-etal-2010-extracting, - title = "Extracting Formulaic and Free Text Clinical Research Articles Metadata using Conditional Random Fields", - author = "Lin, Sein and - Ng, Jun-Ping and - Pradhan, Shreyasee and - Shah, Jatin and - Pietrobon, Ricardo and - Kan, Min-Yen", - editor = "Dalianis, Hercules and - Hassel, Martin and - Nilsson, Gunnar", - booktitle = "Proceedings of the {NAACL} {HLT} 2010 Second Louhi Workshop on Text and Data Mining of Health Documents", - month = jun, - year = "2010", - address = "Los Angeles, California, USA", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W10-1114", - pages = "90--95", -} - - -@inproceedings{kim-etal-2010-semeval, - title = "{S}em{E}val-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles", - author = "Kim, Su Nam and - Medelyan, Olena and - Kan, Min-Yen and - Baldwin, Timothy", - editor = "Erk, Katrin and - Strapparava, Carlo", - booktitle = "Proceedings of the 5th International Workshop on Semantic Evaluation", - month = jul, - year = "2010", - address = "Uppsala, Sweden", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/S10-1004", - pages = "21--26", -} - - -@inproceedings{luong-etal-2010-hybrid, - title = "A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages", - author = "Luong, Minh-Thang and - Nakov, Preslav and - Kan, Min-Yen", - editor = "Li, Hang and - M{\`a}rquez, Llu{\'\i}s", - booktitle = "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing", - month = oct, - year = "2010", - address = "Cambridge, MA", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/D10-1015", - pages = "148--157", -} - - -@inproceedings{hoang-kan-2010-towards, - title = "Towards Automated Related Work Summarization", - author = "Hoang, Cong Duy Vu and - Kan, Min-Yen", - editor = "Huang, Chu-Ren and - Jurafsky, Dan", - booktitle = "Coling 2010: Posters", - month = aug, - year = "2010", - address = "Beijing, China", - publisher = "Coling 2010 Organizing Committee", - url = "https://aclanthology.org/C10-2049", - pages = "427--435", -} - - -@inproceedings{kim-etal-2010-evaluating, - title = "Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extraction", - author = "Kim, Su Nam and - Baldwin, Timothy and - Kan, Min-Yen", - editor = "Huang, Chu-Ren and - Jurafsky, Dan", - booktitle = "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)", - month = aug, - year = "2010", - address = "Beijing, China", - publisher = "Coling 2010 Organizing Committee", - url = "https://aclanthology.org/C10-1065", - pages = "572--580", -} - - -@inproceedings{luong-kan-2010-enhancing, - title = "Enhancing Morphological Alignment for Translating Highly Inflected Languages", - author = "Luong, Minh-Thang and - Kan, Min-Yen", - editor = "Huang, Chu-Ren and - Jurafsky, Dan", - booktitle = "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)", - month = aug, - year = "2010", - address = "Beijing, China", - publisher = "Coling 2010 Organizing Committee", - url = "https://aclanthology.org/C10-1084", - pages = "743--751", -} - - -@proceedings{ws-2009-2009-text, - title = "Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries ({NLPIR}4{DL})", - editor = "Kan, Min-Yen and - Teufel, Simone", - month = aug, - year = "2009", - address = "Suntec City, Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W09-3600", -} - - -@inproceedings{kim-kan-2009-examining, - title = "Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles", - author = "Kim, Su Nam and - Kan, Min-Yen", - editor = "Anastasiou, Dimitra and - Hashimoto, Chikara and - Nakov, Preslav and - Kim, Su Nam", - booktitle = "Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications ({MWE} 2009)", - month = aug, - year = "2009", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W09-2902", - pages = "9--16", -} - - -@inproceedings{hoang-etal-2009-examination, - title = "A re-examination of lexical association measures", - author = "Hoang, Hung Huu and - Kim, Su Nam and - Kan, Min-Yen", - editor = "Anastasiou, Dimitra and - Hashimoto, Chikara and - Nakov, Preslav and - Kim, Su Nam", - booktitle = "Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications ({MWE} 2009)", - month = aug, - year = "2009", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W09-2905", - pages = "31--39", -} - - -@inproceedings{kim-etal-2009-extracting, - title = "Extracting Domain-Specific Words - A Statistical Approach", - author = "Kim, Su Nam and - Baldwin, Timothy and - Kan, Min-Yen", - editor = "Pizzato, Luiz Augusto and - Schwitter, Rolf", - booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2009", - month = dec, - year = "2009", - address = "Sydney, Australia", - url = "https://aclanthology.org/U09-1013", - pages = "94--98", -} - - -@inproceedings{setiawan-etal-2009-topological, - title = "Topological Ordering of Function Words in Hierarchical Phrase-based Translation", - author = "Setiawan, Hendra and - Kan, Min-Yen and - Li, Haizhou and - Resnik, Philip", - editor = "Su, Keh-Yih and - Su, Jian and - Wiebe, Janyce and - Li, Haizhou", - booktitle = "Proceedings of the Joint Conference of the 47th Annual Meeting of the {ACL} and the 4th International Joint Conference on Natural Language Processing of the {AFNLP}", - month = aug, - year = "2009", - address = "Suntec, Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P09-1037", - pages = "324--332", -} - - -@inproceedings{lin-etal-2009-recognizing, - title = "Recognizing Implicit Discourse Relations in the {P}enn {D}iscourse {T}reebank", - author = "Lin, Ziheng and - Kan, Min-Yen and - Ng, Hwee Tou", - editor = "Koehn, Philipp and - Mihalcea, Rada", - booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing", - month = aug, - year = "2009", - address = "Singapore", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/D09-1036", - pages = "343--351", -} - - -@inproceedings{bird-etal-2008-acl, - title = "The {ACL} {A}nthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics", - author = "Bird, Steven and - Dale, Robert and - Dorr, Bonnie and - Gibson, Bryan and - Joseph, Mark and - Kan, Min-Yen and - Lee, Dongwon and - Powley, Brett and - Radev, Dragomir and - Tan, Yee Fan", - editor = "Calzolari, Nicoletta and - Choukri, Khalid and - Maegaard, Bente and - Mariani, Joseph and - Odijk, Jan and - Piperidis, Stelios and - Tapias, Daniel", - booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)", - month = may, - year = "2008", - address = "Marrakech, Morocco", - publisher = "European Language Resources Association (ELRA)", - url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/445_paper.pdf", - abstract = "The ACL Anthology is a digital archive of conference and journal papers in natural language processing and computational linguistics. Its primary purpose is to serve as a reference repository of research results, but we believe that it can also be an object of study and a platform for research in its own right. We describe an enriched and standardized reference corpus derived from the ACL Anthology that can be used for research in scholarly document processing. This corpus, which we call the ACL Anthology Reference Corpus (ACL ARC), brings together the recent activities of a number of research groups around the world. Our goal is to make the corpus widely available, and to encourage other researchers to use it as a standard testbed for experiments in both bibliographic and bibliometric research.", -} - - -@inproceedings{councill-etal-2008-parscit, - title = "{P}ars{C}it: an Open-source {CRF} Reference String Parsing Package", - author = "Councill, Isaac and - Giles, C. Lee and - Kan, Min-Yen", - editor = "Calzolari, Nicoletta and - Choukri, Khalid and - Maegaard, Bente and - Mariani, Joseph and - Odijk, Jan and - Piperidis, Stelios and - Tapias, Daniel", - booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)", - month = may, - year = "2008", - address = "Marrakech, Morocco", - publisher = "European Language Resources Association (ELRA)", - url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/166_paper.pdf", - abstract = "We describe ParsCit, a freely available, open-source implementation of a reference string parsing package. At the core of ParsCit is a trained conditional random field (CRF) model used to label the token sequences in the reference string. A heuristic model wraps this core with added functionality to identify reference strings from a plain text file, and to retrieve the citation contexts. The package comes with utilities to run it as a web service or as a standalone utility. We compare ParsCit on three distinct reference string datasets and show that it compares well with other previously published work.", -} - - -@inproceedings{qiu-etal-2008-modeling, - title = "Modeling Context in Scenario Template Creation", - author = "Qiu, Long and - Kan, Min-Yen and - Chua, Tat-Seng", - booktitle = "Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-{I}", - year = "2008", - url = "https://aclanthology.org/I08-1021", -} - - -@inproceedings{lin-kan-2007-timestamped, - title = "Timestamped Graphs: Evolutionary Models of Text for Multi-Document Summarization", - author = "Lin, Ziheng and - Kan, Min-Yen", - editor = "Biemann, Chris and - Matveeva, Irina and - Mihalcea, Rada and - Radev, Dragomir", - booktitle = "Proceedings of the Second Workshop on {T}ext{G}raphs: Graph-Based Algorithms for Natural Language Processing", - year = "2007", - address = "Rochester, NY, USA", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W07-0204", - pages = "25--32", -} - - -@inproceedings{elmacioglu-etal-2007-psnus, - title = "{PSNUS}: Web People Name Disambiguation by Simple Clustering with Rich Features", - author = "Elmacioglu, Ergin and - Tan, Yee Fan and - Yan, Su and - Kan, Min-Yen and - Lee, Dongwon", - editor = "Agirre, Eneko and - M{\`a}rquez, Llu{\'\i}s and - Wicentowski, Richard", - booktitle = "Proceedings of the Fourth International Workshop on Semantic Evaluations ({S}em{E}val-2007)", - month = jun, - year = "2007", - address = "Prague, Czech Republic", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/S07-1058", - pages = "268--271", -} - - -@inproceedings{setiawan-etal-2007-ordering, - title = "Ordering Phrases with Function Words", - author = "Setiawan, Hendra and - Kan, Min-Yen and - Li, Haizhou", - editor = "Zaenen, Annie and - van den Bosch, Antal", - booktitle = "Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics", - month = jun, - year = "2007", - address = "Prague, Czech Republic", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P07-1090", - pages = "712--719", -} - - -@inproceedings{tan-etal-2006-extending, - title = "Extending corpus-based identification of light verb constructions using a supervised learning framework", - author = "Tan, Yee Fan and - Kan, Min-Yen and - Cui, Hang", - booktitle = "Proceedings of the Workshop on Multi-word-expressions in a multilingual context", - year = "2006", - url = "https://aclanthology.org/W06-2407", -} - - -@inproceedings{qiu-etal-2006-paraphrase, - title = "Paraphrase Recognition via Dissimilarity Significance Classification", - author = "Qiu, Long and - Kan, Min-Yen and - Chua, Tat-Seng", - editor = "Jurafsky, Dan and - Gaussier, Eric", - booktitle = "Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing", - month = jul, - year = "2006", - address = "Sydney, Australia", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W06-1603", - pages = "18--26", -} - - -@inproceedings{qiu-etal-2004-public, - title = "A Public Reference Implementation of the {RAP} Anaphora Resolution Algorithm", - author = "Qiu, Long and - Kan, Min-Yen and - Chua, Tat-Seng", - editor = "Lino, Maria Teresa and - Xavier, Maria Francisca and - Ferreira, F{\'a}tima and - Costa, Rute and - Silva, Raquel", - booktitle = "Proceedings of the Fourth International Conference on Language Resources and Evaluation ({LREC}{'}04)", - month = may, - year = "2004", - address = "Lisbon, Portugal", - publisher = "European Language Resources Association (ELRA)", - url = "http://www.lrec-conf.org/proceedings/lrec2004/pdf/778.pdf", -} - - -@inproceedings{kan-mckeown-2002-corpus, - title = "Corpus-trained Text Generation for Summarization", - author = "Kan, Min-Yen and - McKeown, Kathleen R.", - editor = "McKeown, Kathleen", - booktitle = "Proceedings of the International Natural Language Generation Conference", - month = jul, - year = "2002", - address = "Harriman, New York, USA", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W02-2101", - pages = "1--8", -} - - -@inproceedings{kan-etal-2002-using, - title = "Using the Annotated Bibliography as a Resource for Indicative Summarization", - author = "Kan, Min-Yen and - Klavans, Judith L. and - McKeown, Kathleen R.", - editor = "Gonz{\'a}lez Rodr{\'\i}guez, Manuel and - Suarez Araujo, Carmen Paz", - booktitle = "Proceedings of the Third International Conference on Language Resources and Evaluation ({LREC}{'}02)", - month = may, - year = "2002", - address = "Las Palmas, Canary Islands - Spain", - publisher = "European Language Resources Association (ELRA)", - url = "http://www.lrec-conf.org/proceedings/lrec2002/pdf/7.pdf", -} - - -@inproceedings{kan-etal-2001-applying, - title = "Applying Natural Language Generation to Indicative Summarization", - author = "Kan, Min-Yen and - McKeown, Kathleen R. and - Klavans, Judith L.", - editor = "Horacek, Helmut and - Nicolov, Nicolas and - Wanner, Leo", - booktitle = "Proceedings of the {ACL} 2001 Eighth {E}uropean Workshop on Natural Language Generation ({EWNLG})", - year = "2001", - address = "Toulouse, France", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/W01-0813", -} - - -@inproceedings{kan-etal-1998-linear, - title = "Linear Segmentation and Segment Significance", - author = "Kan, Min-Yen and - Klavans, Judith L. and - McKeown, Kathleen R.", - booktitle = "Sixth Workshop on Very Large Corpora", - year = "1998", - url = "https://aclanthology.org/W98-1123", -} - - -@inproceedings{klavans-kan-1998-role-verbs, - title = "Role of Verbs in Document Analysis", - author = "Klavans, Judith L. and - Kan, Min-Yen", - booktitle = "36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1", - month = aug, - year = "1998", - address = "Montreal, Quebec, Canada", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/P98-1112", - doi = "10.3115/980845.980959", - pages = "680--686", -} - From c51ca3397ce080ba368b644900d41ac10c96b4cc Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Thu, 11 Jul 2024 08:01:29 +0000 Subject: [PATCH 15/22] fix min's roles --- content/authors/min/_index.md | 4 ++-- content/people/index.md | 3 +-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/content/authors/min/_index.md b/content/authors/min/_index.md index 083dbef..0257be5 100644 --- a/content/authors/min/_index.md +++ b/content/authors/min/_index.md @@ -76,8 +76,8 @@ highlight_name: false # Organizational groups that you belong to (for People widget) # Set this to `[]` or comment out if you are not using People widget. user_groups: - - Principal Investigators - - Researchers + - Principal Investigators / Staff +# - Researchers --- Min-Yen Kan (BS;MS;PhD Columbia Univ.; SACM, SIEEE) is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore. Min is an active member of the Association of Computational Linguistics (ACL), currently serving as a co-chair for the ACL Ethics Committee, and previously as the ACL Anthology Director (2008–2018). He is an associate editor for Information Retrieval and the survey editor for the Journal of AI Research (JAIR). diff --git a/content/people/index.md b/content/people/index.md index efb63e4..a7a2cf4 100644 --- a/content/people/index.md +++ b/content/people/index.md @@ -11,8 +11,7 @@ sections: # Choose which groups/teams of users to display. # Edit `user_groups` in each user's profile to add them to one or more of these groups. user_groups: - - Principal Investigators - - Staff + - Principal Investigator / Staff - Graduate Students - Undergraduate Students - Visitors / Interns From 6a72a1fe87f6858706a20edbae7a8213954e67da Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Thu, 11 Jul 2024 08:03:48 +0000 Subject: [PATCH 16/22] fix user role (2) --- content/authors/min/_index.md | 2 +- content/people/index.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/content/authors/min/_index.md b/content/authors/min/_index.md index 0257be5..14d6ab2 100644 --- a/content/authors/min/_index.md +++ b/content/authors/min/_index.md @@ -76,7 +76,7 @@ highlight_name: false # Organizational groups that you belong to (for People widget) # Set this to `[]` or comment out if you are not using People widget. user_groups: - - Principal Investigators / Staff + - Principal Investigator / Staff # - Researchers --- diff --git a/content/people/index.md b/content/people/index.md index a7a2cf4..a37488c 100644 --- a/content/people/index.md +++ b/content/people/index.md @@ -19,7 +19,7 @@ sections: sort_by: Params.last_name sort_ascending: true design: - show_interests: false + show_interests: true show_role: true show_social: true --- \ No newline at end of file From d0b3c0009405b88e09de880ca1bc096b2d0dc895 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Fri, 12 Jul 2024 07:37:22 +0000 Subject: [PATCH 17/22] new pub --- publications.bib | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/publications.bib b/publications.bib index e69de29..708b62c 100644 --- a/publications.bib +++ b/publications.bib @@ -0,0 +1,10 @@ +@ARTICLE{8743365, + author={An, Ya-Hui and Pan, Liangming and Kan, Min-Yen and Dong, Qiang and Fu, Yan}, + journal={IEEE Access}, + title={Resource Mention Extraction for MOOC Discussion Forums}, + year={2019}, + volume={7}, + number={}, + pages={87887-87900}, + keywords={Context;Task analysis;Tagging;Message systems;Discussion forums;Context modeling;Semantics;Artificial intelligence;deep learning;hyperlinking;learning resources;MOOC discussion forums;name entity recognition}, + doi={10.1109/ACCESS.2019.2924250}} From a764316736c9fd0372b0c39682f32b064b691316 Mon Sep 17 00:00:00 2001 From: knmnyn <217067+knmnyn@users.noreply.github.com> Date: Fri, 12 Jul 2024 07:37:45 +0000 Subject: [PATCH 18/22] content: import publications from Bibtex --- content/publication/8743365/cite.bib | 11 +++++++++++ content/publication/8743365/index.md | 19 +++++++++++++++++++ 2 files changed, 30 insertions(+) create mode 100644 content/publication/8743365/cite.bib create mode 100644 content/publication/8743365/index.md diff --git a/content/publication/8743365/cite.bib b/content/publication/8743365/cite.bib new file mode 100644 index 0000000..b164507 --- /dev/null +++ b/content/publication/8743365/cite.bib @@ -0,0 +1,11 @@ +@article{8743365, + author = {An, Ya-Hui and Pan, Liangming and Kan, Min-Yen and Dong, Qiang and Fu, Yan}, + doi = {10.1109/ACCESS.2019.2924250}, + journal = {IEEE Access}, + keywords = {Context;Task analysis;Tagging;Message systems;Discussion forums;Context modeling;Semantics;Artificial intelligence;deep learning;hyperlinking;learning resources;MOOC discussion forums;name entity recognition}, + number = {}, + pages = {87887-87900}, + title = {Resource Mention Extraction for MOOC Discussion Forums}, + volume = {7}, + year = {2019} +} diff --git a/content/publication/8743365/index.md b/content/publication/8743365/index.md new file mode 100644 index 0000000..df38c16 --- /dev/null +++ b/content/publication/8743365/index.md @@ -0,0 +1,19 @@ +--- +title: Resource Mention Extraction for MOOC Discussion Forums +authors: +- Ya-Hui An +- Liangming Pan +- Min-Yen Kan +- Qiang Dong +- Yan Fu +date: '2019-01-01' +publishDate: '2024-07-12T07:37:44.756935Z' +publication_types: +- article-journal +publication: '*IEEE Access*' +doi: 10.1109/ACCESS.2019.2924250 +tags: +- Context;Task analysis;Tagging;Message systems;Discussion forums;Context modeling;Semantics;Artificial + intelligence;deep learning;hyperlinking;learning resources;MOOC discussion forums;name + entity recognition +--- From dc814c803eb5c4055f9d607037ebb564ed08ad57 Mon Sep 17 00:00:00 2001 From: Victor Li Chuang <54843252+xiaowudeshen@users.noreply.github.com> Date: Sat, 13 Jul 2024 05:52:35 +0000 Subject: [PATCH 19/22] Test to add new person --- content/authors/jason/_index.md | 62 +++++++++++++++++++++++++++++++ content/authors/jason/acatar.jpg | Bin 0 -> 8071559 bytes 2 files changed, 62 insertions(+) create mode 100644 content/authors/jason/_index.md create mode 100644 content/authors/jason/acatar.jpg diff --git a/content/authors/jason/_index.md b/content/authors/jason/_index.md new file mode 100644 index 0000000..78b3832 --- /dev/null +++ b/content/authors/jason/_index.md @@ -0,0 +1,62 @@ +--- +# Display name +title: Jason Qiu + +# Full Name (for SEO) +first_name: Jason +last_name: Qiu + +# Is this the primary user of the site? +superuser: true + +# Role/position +role: Undergraduate Student + +# Organizations/Affiliations +organizations: + - name: National University of Singapore, School of Computing + url: 'http://www.comp.nus.edu.sg' + +# Short bio (displayed in user profile at end of posts) +bio: FYP student + +interests: + - Artificial Intelligence + - Information Retrieval + + + + +# Social/Academic Networking +# For available icons, see: https://docs.hugoblox.com/getting-started/page-builder/#icons +# For an email link, use "fas" icon pack, "envelope" icon, and a link in the +# form "mailto:your-email@example.com" or "#contact" for contact widget. +social: + - icon: house + icon_pack: fas + link: https://www.linkedin.com/in/jasonqiu212/ + - icon: envelope + icon_pack: fas + link: 'mailto:jason.qiu@u.nus.edu' + +# Link to a PDF of your resume/CV from the About widget. +# To enable, copy your resume/CV to `static/files/cv.pdf` and uncomment the lines below. +# - icon: cv +# icon_pack: ai +# link: files/cv.pdf + +# Enter email to display Gravatar (if Gravatar enabled in Config) +email: 'jason.qiu@u.nus.edu' + +# Highlight the author in author lists? 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content/authors/jason/{acatar.jpg => avatar.jpg} (100%) diff --git a/content/authors/jason/acatar.jpg b/content/authors/jason/avatar.jpg similarity index 100% rename from content/authors/jason/acatar.jpg rename to content/authors/jason/avatar.jpg From 9db2a9a7a20c8ab6cefff9a3f3b5a77305d5668b Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Sat, 13 Jul 2024 06:15:38 +0000 Subject: [PATCH 21/22] pull in the remainder of the An et al '19 paper --- content/publication/8743365/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/publication/8743365/index.md b/content/publication/8743365/index.md index df38c16..92f14cc 100644 --- a/content/publication/8743365/index.md +++ b/content/publication/8743365/index.md @@ -3,7 +3,7 @@ title: Resource Mention Extraction for MOOC Discussion Forums authors: - Ya-Hui An - Liangming Pan -- Min-Yen Kan +- min - Qiang Dong - Yan Fu date: '2019-01-01' From 4fe4a41731a241b5f08b2f304f59641becd10eb6 Mon Sep 17 00:00:00 2001 From: Min-Yen Kan Date: Sat, 13 Jul 2024 06:20:41 +0000 Subject: [PATCH 22/22] Update Min's bio --- content/authors/min/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/authors/min/_index.md b/content/authors/min/_index.md index 14d6ab2..5e403d0 100644 --- a/content/authors/min/_index.md +++ b/content/authors/min/_index.md @@ -18,7 +18,7 @@ organizations: url: 'http://www.comp.nus.edu.sg' # Short bio (displayed in user profile at end of posts) -bio: My research interests include distributed robotics, mobile computing and programmable matter. +bio: WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing. interests: - Artificial Intelligence @@ -80,7 +80,7 @@ user_groups: # - Researchers --- -Min-Yen Kan (BS;MS;PhD Columbia Univ.; SACM, SIEEE) is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore. Min is an active member of the Association of Computational Linguistics (ACL), currently serving as a co-chair for the ACL Ethics Committee, and previously as the ACL Anthology Director (2008–2018). He is an associate editor for Information Retrieval and the survey editor for the Journal of AI Research (JAIR). +Min-Yen Kan (BS;MS;PhD Columbia Univ.; SACM, SIEEE) is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore. Min is an active member of the Association of Computational Linguistics (ACL), currently serving as a co-chair for the ACL Ethics Committee (ACL AEC), and previously as the ACL Anthology Director (2008–2018). He is an associate editor for Information Retrieval and the survey editor for the Journal of AI Research (JAIR). His research interests include digital libraries, natural language processing and information retrieval. He was recognized as a distinguished speaker by the ACM for natural language processing and digital libraries research. Specific projects include work in the areas of scientific discourse analysis, fact verification, full-text literature mining, lexical semantics and large language models. He leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) http://wing.comp.nus.edu.sg/

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