{"id":13675473,"url":"https://github.com/snakeztc/NeuralDialogPapers","last_synced_at":"2025-04-28T23:30:49.166Z","repository":{"id":44447100,"uuid":"67437371","full_name":"snakeztc/NeuralDialogPapers","owner":"snakeztc","description":"Summary of deep learning models for dialog systems (Tiancheng Zhao LTI, CMU)","archived":false,"fork":false,"pushed_at":"2020-07-08T03:00:17.000Z","size":48,"stargazers_count":651,"open_issues_count":0,"forks_count":136,"subscribers_count":68,"default_branch":"master","last_synced_at":"2024-11-11T16:41:49.612Z","etag":null,"topics":["chatbot","deep-learning","language","neural-network","reinforcement-learning"],"latest_commit_sha":null,"homepage":"https://snakeztc.github.io/NeuralDialogPapers","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/snakeztc.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-09-05T16:55:53.000Z","updated_at":"2024-07-10T15:14:58.000Z","dependencies_parsed_at":"2022-08-24T10:00:17.677Z","dependency_job_id":null,"html_url":"https://github.com/snakeztc/NeuralDialogPapers","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/snakeztc%2FNeuralDialogPapers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/snakeztc%2FNeuralDialogPapers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/snakeztc%2FNeuralDialogPapers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/snakeztc%2FNeuralDialogPapers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/snakeztc","download_url":"https://codeload.github.com/snakeztc/NeuralDialogPapers/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251404425,"owners_count":21584090,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["chatbot","deep-learning","language","neural-network","reinforcement-learning"],"created_at":"2024-08-02T12:00:43.563Z","updated_at":"2025-04-28T23:30:44.157Z","avatar_url":"https://github.com/snakeztc.png","language":null,"readme":"# Neural Network Dialog System Papers\nA list of papers about creating dialog systems using deep nets! **Please feel free to add an issue or pull request for missing papers**.\n\n# Bookmarks\n  * [Task Bots](#task-bots)\n    * General\n    * Multidomain \u0026 Domain Adaptation\n    * User Simulator\n    * Reinforcement Learning and Adversarial \n  * [Chat Bots](#chat-bots)\n    * General\n    * Retrieval Methods\n    * Rich Dialog Context\n    * Diversity\n    * Inteprebility\n\n## Task Bots\n### General \n* [Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks](http://arxiv.org/abs/1609.01462v1), Bing Liu, *arXiv*, 2016\n\n* [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](http://arxiv.org/abs/1609.01454v1), Bing Liu, *arXiv*, 2016\n\n* [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://arxiv.org/abs/1604.04562) Tsung-Hsien Wen et al, 2016\n\n* [Conditional Generation and Snapshot Learning in Neural Dialogue Systems](https://arxiv.org/abs/1606.03352) Tsung-Hsien Wen et al, 2016\n\n* [Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue](http://media.wix.com/ugd/b6d786_137894b7b3a341a09ed0c0b45b46dbb6.pdf) Ryan Lowe et al., 2016\n\n* [End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning](http://arxiv.org/pdf/1606.01269v1.pdf), Jason D. Williams et al., 2016\n\n* [End-to-End Reinforcement Learning of Dialogue Agents for Information Access](http://arxiv.org/abs/1609.00777) Bhuwan Dhingra et al., 2016\n\n* [End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager](https://arxiv.org/abs/1612.00913) Xuesong Yang et al., 2016\n\n* [Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning](https://arxiv.org/abs/1702.03274) Jason D. Williams et al., 2017\n\n* [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings](https://arxiv.org/abs/1704.07130) He He et al., 2017\n\n* [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://arxiv.org/abs/1705.05414) M Eric et al., 2017\n\n* [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://arxiv.org/abs/1706.05125) Mike Lewis et al., 2017\n\n* [Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability](https://arxiv.org/abs/1706.08476) Tiancheng Zhao et al., 2017\n\n* [An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog](https://arxiv.org/pdf/1708.05956.pdf) Liu Bing et al., 2017\n\n* [End-to-End Recurrent Entity Network for Entity-Value Independent Goal-Oriented Dialog Learning](http://workshop.colips.org/dstc6/papers/track1_paper02_wu.pdf) CS Wu et al 2017\n) \n* [Toward Continual Learning for Conversational Agents](https://arxiv.org/pdf/1712.09943.pdf) S Lee 2017\n\n* [Building a Conversational Agent Overnight with Dialogue Self-Play](https://arxiv.org/abs/1801.04871) Pararth Shah et al 2018\n\n* [Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architecture](https://www.comp.nus.edu.sg/~xiangnan/papers/acl18-sequicity.pdf) Wenqiang Lei et al 2018\n\n* [Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems](https://arxiv.org/abs/1804.08217) Andrea Madotto et al 2018\n\n\n### Multidomain \u0026 Domain Adaptation\n* [Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning](https://arxiv.org/pdf/1706.06210.pdf) Paweł et al., 2017\n\n* [Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment](https://arxiv.org/abs/1804.07691) Kaixiang Mo et al. 2018\n\n* [Zero-Shot Dialog Generation with Cross-Domain Latent Actions](https://arxiv.org/abs/1805.04803) Tiancheng Zhao et al 2018\n\n### User Simulator\n\n* [Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System](http://mi.eng.cam.ac.uk/~sjy/papers/stwy07.pdf) Jost Schatzmann 2007\n\n* [A User Simulator for Task-Completion Dialogues](https://arxiv.org/abs/1612.05688) Xinjun Li et al., 2016\n\n* [A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems](https://arxiv.org/pdf/1607.00070.pdf) Layla El Asri 2016\n\n* [Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems](https://arxiv.org/pdf/1805.06966.pdf) Florian L. Kreyssig 2018\n\n\n### Reinforcement Learning and Adversarial \n* [Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning](https://arxiv.org/pdf/1606.02560v1.pdf) Tiancheng Zhao et al., 2016\n\n* [Deep Reinforcement Learning for Dialogue Generation](https://arxiv.org/abs/1606.01541) Jiwei Li et al., *arXiv*, 2016\n\n* [Adversarial Learning for Neural Dialogue Generation](https://arxiv.org/abs/1701.06547) Jiwei Li et al., 2017\n\n* [A deep reinforcement learning chatbot](https://arxiv.org/abs/1709.02349) Serban et al 2017\n\n* [End-to-end Adversarial Learning for Generative Conversational Agents](https://arxiv.org/abs/1711.10122) Ludwig, O. 2017. \n\n* [Strategic Dialogue Management via Deep Reinforcement Learning](https://arxiv.org/abs/1511.08099) Heriberto Cuayáhuitl et al., 2015\n\n* [Generating Text with Deep Reinforcement Learning](http://arxiv.org/abs/1510.09202), Hongyu Guo, *arXiv*, 2015\n\n* [Deep Reinforcement Learning with a Natural Language Action Space](http://arxiv.org/abs/1511.04636v5), Ji He et al., *arXiv*, 2016.\n\n* [Language Understanding for Text-based Games using Deep Reinforcement Learning](https://arxiv.org/abs/1506.08941), Karthik Narasimhan *arXiv*, 2016\n\n* [Deep reinforcement learning for dialogue generation](https://arxiv.org/abs/1606.01541) Jiwei Li et al., 2016\n\n* [End-to-end task-completion neural dialogue systems](https://arxiv.org/abs/1703.01008) Xiujun Li et al., 2017\n\n* [Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning](https://arxiv.org/abs/1706.06210) Paweł Budzianowski et al., 2017\n\n* [Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management](https://arxiv.org/abs/1707.00130) Pei-Hao Su et al., 2017\n\n* [Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning](https://arxiv.org/abs/1704.03084) Baolin Peng et al., 2017\n\n* [Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning](https://arxiv.org/abs/1801.06176) Baolin Peng et al 2018\n\n* [Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog](https://arxiv.org/abs/1805.03257) Jianping Zhang et al 2018\n\n* [Adversarial Learning of Task-Oriented Neural Dialog Models](https://arxiv.org/abs/1805.11762) Bing Liu et al 2018.\n\n## Chat Bots\n### General\n* [A Neural Conversational Model](http://arxiv.org/abs/1506.05869) Oriol Vinyals et al., *arXiv* 2015]\n\n* [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses∗](https://arxiv.org/pdf/1506.06714v1.pdf) \nAlessandro Sordoni et al., *arXiv* 2015]\n\n* [Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation](https://arxiv.org/pdf/1606.00776v2.pdf) Iulian Vlad Serban et al., *arXiv* 2016s\n\n* [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://arxiv.org/abs/1605.06069) Iulian Vlad Serban et al., 2016\n\n* [Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents](https://arxiv.org/abs/1612.03929) Nabiha Asghar et al., 2016\n\n* [Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation](http://www.bigdatalab.ac.cn/~junxu/publications/IJCAI2018-DialogueGen.pdf)\n\n* [Multi-turn Dialogue Response Generation in an Adversarial Learning Framework](https://arxiv.org/abs/1805.11752) - Combining GAN with MLE in the objective.\n\n* [Improving Variational Encoder-Decoders in Dialogue Generation](https://arxiv.org/abs/1802.02032) X Shen et al 2018.\n\n* [MojiTalk: Generating Emotional Responses at Scale](https://arxiv.org/abs/1711.04090) Xianda Zhou et al 2018\n\n* [Exemplar Encoder-Decoder for Neural Conversation Generation](http://aclweb.org/anthology/P18-1123) Gaurav Pandey et al 2018\n\n* Coupled Context Modeling for Deep Chit-Chat: Towards Conversations between Human and Computer(http://www.ruiyan.me/pubs/KDD2018Yan.pdf) Rui Yan et al KDD 2018.\n\n* [Variational Autoregressive Decoder for Neural Response Generation](http://aclweb.org/anthology/D18-1354) Jiachen Du et al 2018.\n\n### Retrieval Methods\n* [Multi-view Response Selection for Human-Computer Conversation](http://www.aclweb.org/anthology/D16-1036) Xiangyang Zhou et al 2016\n\n* [Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots](https://arxiv.org/abs/1612.01627) Yu Wu 2017\n\n* [Modeling multi-turn conversation with deep utterance aggregation](https://arxiv.org/abs/1806.09102) Zhuosheng Zhang et al 2018\n\n* [Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network](http://www.aclweb.org/anthology/P18-1103) Xiangyang Zhang et al 2018.\n\n### Rich Dialog Context\n* [A Persona-Based Neural Conversation Model](https://arxiv.org/abs/1603.06155) Jiwei Li et al, *arXiv*, 2016\n\n* [Conversational Contextual Cues: The Case of Personalization and History for Response Ranking](http://arxiv.org/pdf/1606.00372v1.pdf) Rami Al-Rfou et al., 2016\n\n* [Augmenting End-to-End Dialog Systems with Commonsense Knowledge](https://arxiv.org/abs/1709.05453) Tom Young et al., 2017\n\n* [Topic Compositional Neural Language Model](https://arxiv.org/abs/1712.09783) W Wang et al 2017\n\n* [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://arxiv.org/abs/1801.07243) Zhang, Saizheng, et al., 2018\n\nSome of  the models are evaluated at CNN/Daily Mail and Children's Book Test (CBT) corpora.\n\n* [Teaching Machines to Read and Comprehend](https://arxiv.org/abs/1506.03340), Karl Moritz Hermann et al., *arXiv*, 2015.\n  * Deep LSTM/Attentive Reader/Impatient Reader\n\n* [Text Understanding with the Attention Sum Reader Network](https://arxiv.org/abs/1603.01547), Rudolf Kadlec et al., *arXiv*, 2016.\n\n* [The Goldlocks Principle: Reading Children's Books With Explicit Memory Representations](https://arxiv.org/abs/1511.02301), Felix Hill., *arXiv*, 2016.\n  * Memory Network\n\n* [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895v5), Sainbayar Sukhbaatar et al., *arXiv*, 2015.\n\n* [Dynamic Entity Representation with Max-pooling Improves Machine Reading](http://www.cl.ecei.tohoku.ac.jp/publications/2016/kobayashi-dynamic-entity-naacl2016.pdf), Sosuke Kobayashi et al., *arXiv*, 2016.\n\n* [Gated-Attention Readers for Text Comprehension](https://arxiv.org/abs/1606.01549), Bhuwan Dhingra et al., *arXiv*, 2016.\n\n* [Iterative Alternating Neural Attention for Machine Reading](http://arxiv.org/abs/1606.02245v3), Alessandro Sordoni et al., *arXiv*, 2016.\n\n* [A Neural Network Approach to Context-Senstive Generation of Conversational Responses](https://michaelauli.github.io/papers/chitchat.pdf), Alessandro Sordoni et al, 2015\n\n* [Attention-over-Attention Neural Networks for Reading Comprehension](https://arxiv.org/abs/1607.04423) Yiming Cui et al., *arXiv* 2016\n\n* [Hierarchical Recurrent Attention Network for Response Generation](https://arxiv.org/pdf/1701.07149.pdf) Chen Xing et al., 2017\n\n* [How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models](http://www.aclweb.org/anthology/P17-2036) Zhiliang Tian et al., 2017\n\n* [Chat More: Deepening and Widening the Chatting Topic via A\nDeep Model](http://coai.cs.tsinghua.edu.cn/hml/media/files/2018SIGIR_Wangwenjie.pdf) Wenjie Wang et al., 2018\n\n### Diversity\n* [A Diversity-Promoting Objective Function for Neural Conversation Models](http://www.aclweb.org/anthology/N16-1014) Jiwei Li et al. 2016\n\n* [A Simple, Fast Diverse Decoding Algorithm for Neural Generation](https://arxiv.org/abs/1611.08562) Jiwei Li et al., 2016\n\n* [Data Distillation for Controlling Specificity in Dialogue Generation](https://arxiv.org/abs/1702.06703) Jiwei Li et al., 2017\n\n* [Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models](https://arxiv.org/abs/1701.03185) Louis Shao et al., 2017\n\n* [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://arxiv.org/abs/1703.10960) Tiancheng Zhao et al., 2017\n\n* [Latent variable dialogue models and their diversity](https://arxiv.org/abs/1702.05962) Cao, Kris et al 2017\n\n* [DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder](https://arxiv.org/abs/1805.12352) Xiaodong Gu et al 2018\n\n* [Towards a Neural Conversation Model with Diversity Net Using Determinantal Point Processes](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17115) Yiping Song et al 2018\n\n### Inteprebility\n* [Latent intention dialogue models](https://arxiv.org/abs/1705.10229) Tsung-Hsien Wen et al., 2017\n\n* [Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation](https://arxiv.org/abs/1804.08069) Tiancheng Zhao et al., 2018\n\n* [Learning to Control the Specificity in Neural Response Generation](http://aclweb.org/anthology/P18-1102) Ruqing Zhang et al 2018.\n\n\n\n\n\n","funding_links":[],"categories":["Paper Connect","Tool \u0026 Repository","NLP"],"sub_categories":["Korean"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnakeztc%2FNeuralDialogPapers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsnakeztc%2FNeuralDialogPapers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnakeztc%2FNeuralDialogPapers/lists"}