Papers on Knowledge-based Machine Reading Comprehension.

A list of recent papers about Knowledge-based Machine Reading Comprehension (KMRC).

Contributed by Luxi Xing and Yuqiang Xie.

Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China.

Update on Mar. 4, 2020.

(We will continuously update this list.)

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Content

1. Survey
2. Cloze Style Tasks
3. Span Extraction Tasks
4. Multiple Choice Tasks
5. Generation Tasks
6. Datasets

Survey Papers

1. Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches. 2019. [paper / note]

   Authors: Shane Storks, Qianzi Gao, Joyce Y. Chai

2. Neural Machine Reading Comprehension: Methods and Trends. 2019. [paper]

   Authors: Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang

3. Machine Reading Comprehension: a Literature Review. 2019. [paper]

   Authors: Xin Zhang, An Yang, Sujian Li, Yizhong Wang

4. Natural Language QA Approaches using Reasoning with External Knowledge. 2020. [paper]

   Authors: Chitta Baral, Pratyay Banerjee, Kuntal Pal, Arindam Mitra

Cloze Style Tasks

Title	Publish	Tasks	Links
Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension	ACL 2017	SCT	paper
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions	EMNLP 2017	Rare Entity Prediction	paper
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge	ACL 2018	Common Nouns	paper note
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task	COLING 2018	SCT	paper
Incorporating Structured Commonsense Knowledge in Story Completion	AAAI 2018	SCT	paper
Story Ending Prediction by Transferable BERT	IJCAI 2019	SCT	paper

Span Extraction Tasks

Title	Publish	Tasks	Links
Dynamic Integration of Background Knowledge in Neural NLU Systems	2018	SQuAD/ TriviaQA	paper note
Explicit Utilization of General Knowledge in Machine Reading Comprehension	ACL 2019	SQuAD	paper note
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension	ACL 2019	SQuAD/ ReCoRD	paper note
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network	EMNLP 2019 short	ReCoRD	paper
+ SG-Net: Syntax-Guided Machine Reading Comprehension	AAAI 2020	SQuAD2.0 RACE	paper
+ Semantics-aware BERT for Language Understanding	AAAI 2020	SQuAD2.0	paper

• + indicates works regarding injecting knowledge to improve performance on the datasets not included in this summary.

Multiple Choice Tasks

Title	Publish	Tasks	Links
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension	SemEval 2018	SemEval-2018 Task 11	paper code
Improving Question Answering by Commonsense-Based Pre-Training	AAAI 2019	ARC/ OpenBookQA/ SemEval-2018 Task 11	paper
Improving Machine Reading Comprehension with General Reading Strategies	NAACL 2019	ARC/ OpenBookQA/ MCTest/ SemEval-2018 Task 11/ SCT/ MultiRC	paper code
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs	NAACL 2019	story commonsense	paper code
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning	CIKM 2019	SemEval-2018 Task 11 / SCT	paper
Explain Yourself! Leveraging Language Models for Commonsense Reasoning	ACL 2019	CSQA	paper code note
Careful Selection of Knowledge to solve Open Book Question Answering	ACL 2019	OpenBookQA	paper
Improving Question Answering with External Knowledge	EMNLP MRQA 2019	ARC/ OpenBookQA	paper note
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning	EMNLP 2019	CSQA	paper code note
What’s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering	EMNLP 2019	OpenBookQA	paper note
BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge	EMNLP COIN 2019	MCScripts v2	paper
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models	2019	CSQA WSC	paper
Abductive Reasoning as Self-Supervision for Common Sense Question Answering	2019	Swag HellaSwag	paper
Exploring ways to incorporate additional knowledge to improve Natural Language Commonsense Question Answering	2019	ANLI/ SocialIQA	paper note
Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering	AAAI 2020	CSQA	paper note
K-ADAPTER: Infusing Knowledge into Pre-Trained Models with Adapters	2020	CosmosQA	paper note

Generation Tasks

Also known as Free-form Answer Tasks

Title	Publish	Tasks	Links
Commonsense for Generative Multi-Hop Question Answering Tasks	EMNLP 2018	NarrativeQA/ WikiHop	paper code note
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction	ACL 2019	Atomic	paper code note
Incorporating External Knowledge into Machine Reading for Generative Question Answering	EMNLP 2019	MS MARCO	paper note
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder	EMNLP 2019	Event2Mind	paper
Dynamic Knowledge Graph Construction for Zero-shot Commonsense Question Answering	2019	SocialIQA StoryCommonsense	paper

Benchmark Datasets

1. [COPA] Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI,2011. [paper / data]

   Authors: Melissa Roemmele, Cosmin Adrian Bejan, Andrew S. Gordon

   • Type: Multiple-Choice;

2. [WSC] The Winograd Schema Challenge. AAAI,2011. [paper /data]

   Authors: Hector J. Levesque, Ernest Davis, Leora Morgenstern

   • Type: Multiple-Choice;

3. [ROCStories; SCT] A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories. NAACL,2016. [paper / data]

   Authors: Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, James Allen

   • Type: Cloze;

4. [NarrativeQA] The NarrativeQA Reading Comprehension Challenge. TACL,2018. [paper / data]

   Authors: Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette

   • Type: Generation;

5. [SemEval-2018 Task 11] MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge. LERC,2018. [paper / data]

   Authors: Simon Ostermann, Ashutosh Modi, Michael Roth, Stefan Thater, Manfred Pinkal

   • Type: Multiple-Choice;

6. [story-commonsense] Modeling Naive Psychology of Characters in Simple Commonsense Stories. ACL,2018. [paper / data]

   Authors: Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight, Yejin Choi

   • Type: Multiple-Choice;

7. Event2Mind: Commonsense Inference on Events, Intents, and Reactions. ACL,2018. [paper / data]

   Authors: Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A. Smith, Yejin Choi

   • Types: Generation;

8. ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI,2019. [paper / data]

   Authors: Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi

   • Types: Generation;

9. [ARC] Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge. 2018. [paper / data]

   Authors: Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord

   • Type: Multiple-Choice;

10. [OpenBookQA] Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering. EMNLP,2018. [paper / data]

    Authors: Todor Mihaylov, Peter Clark, Tushar Khot, Ashish Sabharwal

    • Type: Multiple-Choice;

11. ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension. 2018. [paper / data]

    Authors: Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, Benjamin Van Durme

    • Type: Cloze;

12. CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge. NAACL,2019. [paper / data]

    Authors: Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan Berant

    • Type: Multiple-Choice;

13. ChID: A Large-scale Chinese IDiom Dataset for Cloze Test. ACL,2019. [paper / data]

    Authors: Chujie Zheng, Minlie Huang, Aixin Sun

    • Type: Cloze;

14. [sense-making] Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation. ACL,2019. [paper / data]

    Authors: Cunxiang Wang, Shuailong Liang, Yue Zhang, Xiaonan Li, Tian Gao

    • Type: Multiple-Choice;

15. HellaSwag: Can a Machine Really Finish Your Sentence? ACL,2019. [paper / data]

    Authors: Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, Yejin Choi

    • Type: Multiple-Choice;

16. SocialIQA: Commonsense Reasoning about Social Interactions. EMNLP,2019. [paper / data]

    Authors: Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, Yejin Choi

    • Type: Multiple-Choice;

17. [ANLI] Abductive Commonsense Reasoning. 2019. [paper / data]

    Authors: Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Scott Wen-tau Yih, Yejin Choi

    • Type: Multiple-Choice;

18. Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning. EMNLP,2019. [paper / data]

    Authors: Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

    • Type: Multiple-Choice;

19. CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense. ACL,2019,workshop. [paper / data ]

    Authors: Michael Chen, Mike D’Arcy, Alisa Liu, Jared Fernandez, Doug Downey

    • Type: Multiple-Choice;

20. CommonGen: A Constrained Text Generation Dataset Towards Generative Commonsense Reasoning. 2019. [ paper / data ]

    Authors: Bill Yuchen Lin, Ming Shen, Yu Xing, Pei Zhou, Xiang Ren

    • Type: Generative;

21. QASC: A Dataset for Question Answering via Sentence Composition. 2019. [paper / data ]

    Authors: Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, Ashish Sabharwal

    • Type: Multiple-Choice;

Note: Only consider the benchmark datasets/tasks which require knowledge to complete.

Other Paper List About MRC

thunlp/RCPapers

PhD Thesis on MRC

1. Neural Reading Comprehension and Beyond

   Author: Danqi Chen (Stanford University) Year: 2018

2. 机器阅读理解的关键技术研究

   Author: Bingning Wang (Institute of Automation, Chinese Academy of Sciences) Year: 2018

3. 机器阅读理解与文本问答技术研究

   Author: Minghao Hu (Graduate School of National University of Defense Technology) Year: 2019