A list of papers about creating dialog systems using deep nets! Please feel free to add an issue for suggesting missing good paper.
Mostly the models are evaluated at CNN/Daily Mail and Children's Book Test (CBT) corpora.
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Teaching Machines to Read and Comprehend, Karl Moritz Hermann et al., arXiv, 2015.
- Deep LSTM/Attentive Reader/Impatient Reader
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Text Understanding with the Attention Sum Reader Network, Rudolf Kadlec et al., arXiv, 2016.
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The Goldlocks Principle: Reading Children's Books With Explicit Memory Representations, Felix Hill., arXiv, 2016.
- Memory Network
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End-To-End Memory Networks, Sainbayar Sukhbaatar et al., arXiv, 2015.
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Dynamic Entity Representation with Max-pooling Improves Machine Reading, Sosuke Kobayashi et al., arXiv, 2016.
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Gated-Attention Readers for Text Comprehension, Bhuwan Dhingra et al., arXiv, 2016.
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Iterative Alternating Neural Attention for Machine Reading, Alessandro Sordoni et al., arXiv, 2016.
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A Neural Network Approach to Context-Senstive Generation of Conversational Responses, Alessandro Sordoni et al, 2015
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Attention-over-Attention Neural Networks for Reading Comprehension Yiming Cui et al., arXiv 2016
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Hierarchical Recurrent Attention Network for Response Generation Chen Xing et al., 2017
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How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models Zhiliang Tian et al., 2017
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Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks, Bing Liu, arXiv, 2016
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Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling, Bing Liu, arXiv, 2016
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A Network-based End-to-End Trainable Task-oriented Dialogue System Tsung-Hsien Wen et al, 2016
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Conditional Generation and Snapshot Learning in Neural Dialogue Systems Tsung-Hsien Wen et al, 2016
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Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Ryan Lowe et al., 2016
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End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning, Jason D. Williams et al., 2016
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Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning Tiancheng Zhao et al., 2016
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End-to-End Reinforcement Learning of Dialogue Agents for Information Access Bhuwan Dhingra et al., 2016
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A User Simulator for Task-Completion Dialogues Xinjun Li et al., 2016
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End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager Xuesong Yang et al., 2016
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Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning Jason D. Williams et al., 2017
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Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings He He et al., 2017
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Key-Value Retrieval Networks for Task-Oriented Dialogue M Eric et al., 2017
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Deal or No Deal? End-to-End Learning for Negotiation Dialogues Mike Lewis et al., 2017
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Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability Tiancheng Zhao et al., 2017
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An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog Liu Bing et al., 2017
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Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning Paweł et al., 2017
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End-to-End Recurrent Entity Network for Entity-Value Independent Goal-Oriented Dialog Learning CS Wu et al 2017
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Toward Continual Learning for Conversational Agents S Lee 2017
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Building a Conversational Agent Overnight with Dialogue Self-Play Pararth Shah et al 2018
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A Neural Conversational Model Oriol Vinyals et al., arXiv 2015]
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A Neural Network Approach to Context-Sensitive Generation of Conversational Responses∗ Alessandro Sordoni et al., arXiv 2015]
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Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation Iulian Vlad Serban et al., arXiv 2016s
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A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues Iulian Vlad Serban et al., 2016
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Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents Nabiha Asghar et al., 2016
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Improving Variational Encoder-Decoders in Dialogue Generation X Shen et al 2018.
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A Persona-Based Neural Conversation Model Jiwei Li et al, arXiv, 2016
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Conversational Contextual Cues: The Case of Personalization and History for Response Ranking Rami Al-Rfou et al., 2016
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Augmenting End-to-End Dialog Systems with Commonsense Knowledge Tom Young et al., 2017
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Topic Compositional Neural Language Model W Wang et al 2017
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A Diversity-Promoting Objective Function for Neural Conversation Models Jiwei Li et al. 2016
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A Simple, Fast Diverse Decoding Algorithm for Neural Generation Jiwei Li et al., 2016
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Data Distillation for Controlling Specificity in Dialogue Generation Jiwei Li et al., 2017
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Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models Louis Shao et al., 2017
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Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders Tiancheng Zhao et al., 2017
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Latent variable dialogue models and their diversity Cao, Kris et al 2017
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Deep Reinforcement Learning for Dialogue Generation Jiwei Li et al., arXiv, 2016
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Adversarial Learning for Neural Dialogue Generation Jiwei Li et al., 2017
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A deep reinforcement learning chatbot Serban et al 2017
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End-to-end Adversarial Learning for Generative Conversational Agents Ludwig, O. 2017.
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Strategic Dialogue Management via Deep Reinforcement Learning Heriberto Cuayáhuitl et al., 2015
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Generating Text with Deep Reinforcement Learning, Hongyu Guo, arXiv, 2015
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Deep Reinforcement Learning with a Natural Language Action Space, Ji He et al., arXiv, 2016.
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Language Understanding for Text-based Games using Deep Reinforcement Learning, Karthik Narasimhan arXiv, 2016
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Deep reinforcement learning for dialogue generation Jiwei Li et al., 2016
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End-to-end task-completion neural dialogue systems Xiujun Li et al., 2017
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Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning Paweł Budzianowski et al., 2017
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Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management Pei-Hao Su et al., 2017
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Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning Baolin Peng et al., 2017