Dialogue Generation Papers

A list of recent papers regarding dialog generation.
The papers are organized based on manually-defined bookmarks.
Any suggestions and pull requests are welcome.

Bookmarks

• All Papers
• Dataset
• Reinforcement Learning
• Memory networks
• Recurrent Neural Networks
• Evaluation metrics

All Papers

• OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles, Pierre Lison et al., 2016 (3.36 million subtitles)
• Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models, Iulian V. Serban et al., AAAI, 2015. (500 movies)
• Deep Reinforcement Learning for Dialogue Generation, Jiwei Li et al., arXiv, 2016.
• Dialog-based Language Learning, Jason Weston, Facebook AI Research, arXiv, 2016.
• A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues, Iulian V. Serban et al., arXiv, 2016.
• Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation, Iulian Vlad Serban et al., arXiv, 2016.
• LSTM based Conversation Models, Yi Luan et al., arXiv, 2016.
• End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning, Jason D. Williams and Geoffrey Zweig., Microsoft Research, arXiv, 2016.
• Conversational Contextual Cues: The Case of Personalization and History for Response Ranking, Rami Al-Rfou et al., Google Inc, arXiv, 2016.
• Learning End-to-End Goal-Oriented Dialog, Antoine Bordes and Jason Weston, Facebook AI Research, arXiv, 2016.
• Evaluating Prerequisite Qualities For Learning End-to-End Dialog Systems, Jesse Dodge et al., Facebook AI Research, ICLR 2016.
• The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems, Ryan Lowe et al., SIGDial, 2015. [dataset]
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation, Chia-Wei Liu et al., arXiv, 2016.
• A Survey of Available Corpora For Building Data-Driven Dialogue Systems, Iulian Vlad Serban et al., arXiv, 2015.
• Neural Responding Machine for Short-Text Conversation, Lifeng Shang et al., arXiv, 2015.
• A Neural Conversational Model, Oriol Vinyals et al., arXiv, 2015.
• A Neural Network Approach to Context-Sensitive Generation of Conversational Responses, Alessandro Sordoni et al., NAACL, 2015.
• A Diversity-Promoting Objective Function for Neural Conversation Models, Jiwei Li et al., NAACL, 2016. (Maximum Mutual Information)
• A Persona-Based Neural Conversation Model, Jiwei Li et al., ACL, 2016.
• Neural Net Models for Open-Domain Discourse Coherence, Jiwei Li et al., arXiv, 2016.
• A Network-based End-to-End Trainable Task-oriented Dialogue System, Tsung-Hsien Wen et al., arXiv, 2016.
• SimpleDS: A Simple Deep Reinforcement Learning Dialogue System, Heriberto Cuayahuitl, arXiv, 2016.
• End-To-End Generative Dialogue, Colton Gyulay et al. [code]

Dataset

• A Survey of Available Corpora For Building Data-Driven Dialogue Systems, Iulian Vlad Serban et al., arXiv, 2015.
• OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles, Pierre Lison et al. (3.36 million subtitles)
• Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models, Iulian V. Serban et al., AAAI, 2015. (500 movies)
• Conversational Contextual Cues: The Case of Personalization and History for Response Ranking, Rami Al-Rfou et al., arXiv, 2016. (Reddit comments)
• The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems, Ryan Lowe et al., SIGDial 2015. [dataset]

Reinforcement Learning

• Deep Reinforcement Learning for Dialogue Generation, Jiwei Li et al., arXiv, 2016.
• End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning, Jason D. Williams and Geoffrey Zweig., arXiv, 2016.
• A Network-based End-to-End Trainable Task-oriented Dialogue System, Tsung-Hsien Wen et al., arXiv, 2016.
• SimpleDS: A Simple Deep Reinforcement Learning Dialogue System, Heriberto Cuayahuitl, arXiv, 2016.

Memory Networks

• Evaluating Prerequisite Qualities For Learning End-to-End Dialog Systems, Jesse Dodge et al., Facebook AI Research, ICLR 2016.
• Dialog-based Language Learning, Jason Weston, arXiv, 2016.
• Learning End-to-End Goal-Oriented Dialog, Antoine Bordes and Jason Weston, arXiv, 2016.

Recurrent Neural Networks

• Neural Responding Machine for Short-Text Conversation, Lifeng Shang et al., arXiv, 2015.
• A Neural Conversational Model, Oriol Vinyals et al., arXiv, 2015.
• A Neural Network Approach to Context-Sensitive Generation of Conversational Responses, Alessandro Sordoni et al., NAACL, 2015.
• A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues, Iulian V. Serban et al., arXiv, 2016.
• Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation, Iulian Vlad Serban et al., arXiv, 2016.
• LSTM based Conversation Models, Yi Luan et al., arXiv, 2016.
• End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning, Jason D. Williams and Geoffrey Zweig., arXiv, 2016.

Evaluation metrics

• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation, Chia-Wei Liu et al., arXiv, 2016.