https://github.com/tsenghungchen/dialog-generation-paper

A list of recent papers regarding dialogue generation
https://github.com/tsenghungchen/dialog-generation-paper

dialogue dialogue-generation dialogue-systems reinforcement-learning

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A list of recent papers regarding dialogue generation

• Host: GitHub
• URL: https://github.com/tsenghungchen/dialog-generation-paper
• Owner: tsenghungchen
• Created: 2016-07-12T08:07:41.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2019-04-28T20:19:38.000Z (over 5 years ago)
• Last Synced: 2024-08-03T02:03:39.993Z (4 months ago)
• Topics: dialogue, dialogue-generation, dialogue-systems, reinforcement-learning
• Size: 10.7 KB
• Stars: 271
• Watchers: 29
• Forks: 59
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• Awesome-Paper-List - Dialogue Generation - tsenghungchen-be8abf)  (Natural Language Processing)

README

        
# 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](#all-papers)

  * [Dataset](#dataset)

  * [Reinforcement Learning](#reinforcement-learning)

  * [Memory networks](#memory-networks)

  * [Recurrent Neural Networks](#recurrent-neural-networks)

  * [Evaluation metrics](#evaluation-metrics)

  * [Domain Adaptation](#domain-adaptation)

  * [Variational Autoencoders](#variational-autoencoders)

## All Papers

* [OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles](http://stp.lingfil.uu.se/~joerg/paper/opensubs2016.pdf), Pierre Lison et al., 2016 (3.36 million subtitles)

* [Building End-To-End Dialogue Systems

Using Generative Hierarchical Neural Network Models](https://arxiv.org/pdf/1507.04808.pdf), Iulian V. Serban et al., *AAAI*, 2015. (500 movies)

* [Deep Reinforcement Learning for Dialogue Generation](https://arxiv.org/pdf/1606.01541.pdf), Jiwei Li et al., *arXiv*, 2016.

* [Dialog-based Language Learning](https://arxiv.org/pdf/1604.06045v4.pdf), Jason Weston, Facebook AI Research, *arXiv*, 2016.

* [A Hierarchical Latent Variable Encoder-Decoder

Model for Generating Dialogues](https://arxiv.org/pdf/1605.06069v3.pdf), Iulian V. Serban et al., *arXiv*, 2016.

* [Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation](https://arxiv.org/pdf/1606.00776v2.pdf), Iulian Vlad Serban et al., *arXiv*, 2016.

* [LSTM based Conversation Models](http://arxiv.org/pdf/1603.09457v1.pdf), Yi Luan et al., *arXiv*, 2016.

* [End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning](https://arxiv.org/pdf/1606.01269v1.pdf), Jason D. Williams and Geoffrey Zweig., Microsoft Research, *arXiv*, 2016.

* [Conversational Contextual Cues: The Case of Personalization and History for Response Ranking](https://arxiv.org/pdf/1606.00372v1.pdf), Rami Al-Rfou et al., Google Inc, *arXiv*, 2016.

* [Learning End-to-End Goal-Oriented Dialog](https://arxiv.org/pdf/1605.07683.pdf), Antoine Bordes and Jason Weston, Facebook AI Research, *arXiv*, 2016.

* [Evaluating Prerequisite Qualities For Learning End-to-End Dialog Systems](http://arxiv.org/pdf/1511.06931v6.pdf), Jesse Dodge et al., Facebook AI Research, ICLR 2016.

* [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](http://arxiv.org/pdf/1506.08909v3.pdf), Ryan Lowe et al., *SIGDial*, 2015. [[dataset](https://github.com/rkadlec/ubuntu-ranking-dataset-creator)]

* [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://arxiv.org/pdf/1603.08023v1.pdf), Chia-Wei Liu et al., *arXiv*, 2016.

* [A Survey of Available Corpora For Building Data-Driven Dialogue Systems](http://arxiv.org/pdf/1512.05742v2.pdf), Iulian Vlad Serban et al., *arXiv*, 2015.

* [Neural Responding Machine for Short-Text Conversation](https://arxiv.org/pdf/1503.02364v2.pdf), Lifeng Shang et al., *arXiv*, 2015.

* [A Neural Conversational Model](https://arxiv.org/pdf/1506.05869.pdf), Oriol Vinyals et al., *arXiv*, 2015.

* [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](http://arxiv.org/pdf/1506.06714v1.pdf), Alessandro Sordoni et al., *NAACL*, 2015.

* [A Diversity-Promoting Objective Function for Neural Conversation Models](http://arxiv.org/pdf/1510.03055v3.pdf), Jiwei Li et al., *NAACL*, 2016. (Maximum Mutual Information)

* [A Persona-Based Neural Conversation Model](http://arxiv.org/pdf/1603.06155v2.pdf), Jiwei Li et al., *ACL*, 2016.

* [Neural Net Models for Open-Domain Discourse Coherence](https://arxiv.org/pdf/1606.01545.pdf), Jiwei Li et al., *arXiv*, 2016.

* [A Network-based End-to-End Trainable Task-oriented Dialogue System](http://arxiv.org/pdf/1604.04562v2.pdf), Tsung-Hsien Wen et al., *arXiv*, 2016.

* [SimpleDS: A Simple Deep Reinforcement Learning Dialogue System](http://arxiv.org/pdf/1601.04574v1.pdf), Heriberto Cuayahuitl, *arXiv*, 2016.

* [End-To-End Generative Dialogue](https://github.com/michaelfarrell76/End-To-End-Generative-Dialogue/blob/master/paper/main.pdf), Colton Gyulay et al. [[code](https://github.com/michaelfarrell76/End-To-End-Generative-Dialogue)]

* [A Conditional Variational Framework for Dialog Generation](https://arxiv.org/pdf/1705.00316.pdf), Xiaoyu Shen, *arXiv*, 2017.

* [Towards Interpretable Chit-chat: Open Domain Dialogue Generation with Dialogue Acts](https://openreview.net/pdf?id=Bym0cU1CZ), 2017.

* [Frames: A Corpus for Adding Memory

to Goal-Oriented Dialogue Systems

](http://www.aclweb.org/anthology/W17-5526), Layla El Asri et al., Microsoft Maluuba. SIGDial 2017. [[dataset](http://datasets.maluuba.com/Frames)]

 * [Relevance of Unsupervised Metrics in Task-Oriented Dialogue for

Evaluating Natural Language Generation](https://arxiv.org/pdf/1706.09799.pdf), Shikhar Sharma et al., Microsoft Maluuba. *arXiv* 2017. [[code](https://github.com/Maluuba/nlg-eval)]

## Dataset

 * [A Survey of Available Corpora For Building Data-Driven Dialogue Systems](http://arxiv.org/pdf/1512.05742v2.pdf), Iulian Vlad Serban et al., *arXiv*, 2015.

 * [OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles](http://stp.lingfil.uu.se/~joerg/paper/opensubs2016.pdf), Pierre Lison et al. (3.36 million subtitles)

 * [Building End-To-End Dialogue Systems

Using Generative Hierarchical Neural Network Models](https://arxiv.org/pdf/1507.04808.pdf), Iulian V. Serban et al., *AAAI*, 2015. (500 movies)

 * [Conversational Contextual Cues: The Case of Personalization and History for Response Ranking](https://arxiv.org/pdf/1606.00372v1.pdf), Rami Al-Rfou et al., *arXiv*, 2016. (Reddit comments)

 * [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](http://arxiv.org/pdf/1506.08909v3.pdf), Ryan Lowe et al., SIGDial 2015. [[dataset](https://github.com/rkadlec/ubuntu-ranking-dataset-creator)]

 * [Frames: A Corpus for Adding Memory

to Goal-Oriented Dialogue Systems

](http://www.aclweb.org/anthology/W17-5526), Layla El Asri et al., Microsoft Maluuba. SIGDial 2017. [[dataset](http://datasets.maluuba.com/Frames)]

## Reinforcement Learning

* [Deep Reinforcement Learning for Dialogue Generation](https://arxiv.org/pdf/1606.01541.pdf), Jiwei Li et al., *arXiv*, 2016.

* [End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning](https://arxiv.org/pdf/1606.01269v1.pdf), Jason D. Williams and Geoffrey Zweig., *arXiv*, 2016.

* [A Network-based End-to-End Trainable Task-oriented Dialogue System](http://arxiv.org/pdf/1604.04562v2.pdf), Tsung-Hsien Wen et al., *arXiv*, 2016.

* [SimpleDS: A Simple Deep Reinforcement Learning Dialogue System](http://arxiv.org/pdf/1601.04574v1.pdf), Heriberto Cuayahuitl, *arXiv*, 2016.

* [Towards Interpretable Chit-chat: Open Domain Dialogue Generation with Dialogue Acts](https://openreview.net/pdf?id=Bym0cU1CZ), 2017.

* [End-to-End Task-Completion Neural Dialogue Systems](https://arxiv.org/pdf/1703.01008.pdf), Xiujun Li et al., *arXiv*, 2018.

## Memory Networks

* [Evaluating Prerequisite Qualities For Learning End-to-End Dialog Systems](http://arxiv.org/pdf/1511.06931v6.pdf), Jesse Dodge et al., Facebook AI Research, ICLR 2016.

* [Dialog-based Language Learning](https://arxiv.org/pdf/1604.06045v4.pdf), Jason Weston, *arXiv*, 2016.

* [Learning End-to-End Goal-Oriented Dialog](https://arxiv.org/pdf/1605.07683.pdf), Antoine Bordes and Jason Weston, *arXiv*, 2016.

## Recurrent Neural Networks

* [Neural Responding Machine for Short-Text Conversation](https://arxiv.org/pdf/1503.02364v2.pdf), Lifeng Shang et al., *arXiv*, 2015.

* [A Neural Conversational Model](https://arxiv.org/pdf/1506.05869.pdf), Oriol Vinyals et al., *arXiv*, 2015.

* [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](http://arxiv.org/pdf/1506.06714v1.pdf), Alessandro Sordoni et al., *NAACL*, 2015.

* [A Hierarchical Latent Variable Encoder-Decoder

Model for Generating Dialogues](https://arxiv.org/pdf/1605.06069v3.pdf), Iulian V. Serban et al., *arXiv*, 2016.

* [Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation](https://arxiv.org/pdf/1606.00776v2.pdf), Iulian Vlad Serban et al., *arXiv*, 2016.

* [LSTM based Conversation Models](http://arxiv.org/pdf/1603.09457v1.pdf), Yi Luan et al., *arXiv*, 2016.

* [End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning](https://arxiv.org/pdf/1606.01269v1.pdf), 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](https://arxiv.org/pdf/1603.08023v1.pdf), Chia-Wei Liu et al., *arXiv*, 2016.

 * [Relevance of Unsupervised Metrics in Task-Oriented Dialogue for

Evaluating Natural Language Generation](https://arxiv.org/pdf/1706.09799.pdf), Shikhar Sharma et al., Microsoft Maluuba. *arXiv* 2017. [[code](https://github.com/Maluuba/nlg-eval)]

## Domain Adaptation

* [Multi-domain Neural Network Language Generation for Spoken Dialogue Systems](http://mi.eng.cam.ac.uk/~sjy/papers/wgmr16.pdf), Tsung-Hsien Wen et al.

* [Domain Adaptation with Unlabeled Data for Dialog Act Tagging](http://ttic.uchicago.edu/~klivescu/papers/margolis_etal_danlp2010.pdf), Anna Margolis et al.

* [Learning Domain-Independent Dialogue Policies via Ontology Parameterisation](http://mi.eng.cam.ac.uk/~sjy/papers/wsws15.pdf), Zhuoran Wang et al.

## Variational Autoencoders

* [A Conditional Variational Framework for Dialog Generation](https://arxiv.org/pdf/1705.00316.pdf), Xiaoyu Shen, *arXiv*, 2017.