• Task Bots
  • General
  • Multidomain & Domain Adaptation
  • User Simulator
  • Reinforcement Learning and Adversarial
• Chat Bots
  • General
  • Retrieval Methods
  • Rich Dialog Context
  • Diversity
  • Inteprebility
• Question Answering
  • QA Matching
  • Knowledge Graph Representation
  • KBQA
  • DocQA
  • Question Generation
  • Others

• Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks, Bing Liu, arXiv, 2016

• Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling, Bing Liu, arXiv, 2016

• A Network-based End-to-End Trainable Task-oriented Dialogue System Tsung-Hsien Wen et al, 2016

• Conditional Generation and Snapshot Learning in Neural Dialogue Systems Tsung-Hsien Wen et al, 2016

• Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Ryan Lowe et al., 2016

• End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning, Jason D. Williams et al., 2016

• End-to-End Reinforcement Learning of Dialogue Agents for Information Access Bhuwan Dhingra et al., 2016

• End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager Xuesong Yang et al., 2016

• Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning Jason D. Williams et al., 2017

• Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings He He et al., 2017

• Key-Value Retrieval Networks for Task-Oriented Dialogue M Eric et al., 2017

• Deal or No Deal? End-to-End Learning for Negotiation Dialogues Mike Lewis et al., 2017

• Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability Tiancheng Zhao et al., 2017

• An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog Liu Bing et al., 2017

• End-to-End Recurrent Entity Network for Entity-Value Independent Goal-Oriented Dialog Learning CS Wu et al 2017 )

• Toward Continual Learning for Conversational Agents S Lee 2017

• Building a Conversational Agent Overnight with Dialogue Self-Play Pararth Shah et al 2018

• Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architecture Wenqiang Lei et al 2018

• Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems Andrea Madotto et al 2018

• Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning Paweł et al., 2017

• Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment Kaixiang Mo et al. 2018

• Zero-Shot Dialog Generation with Cross-Domain Latent Actions Tiancheng Zhao et al 2018

• Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System Jost Schatzmann 2007

• A User Simulator for Task-Completion Dialogues Xinjun Li et al., 2016

• A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems Layla El Asri 2016

• Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems Florian L. Kreyssig 2018

• Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning Tiancheng Zhao et al., 2016

• Deep Reinforcement Learning for Dialogue Generation Jiwei Li et al., arXiv, 2016

• Adversarial Learning for Neural Dialogue Generation Jiwei Li et al., 2017

• A deep reinforcement learning chatbot Serban et al 2017

• End-to-end Adversarial Learning for Generative Conversational Agents Ludwig, O. 2017.

• Strategic Dialogue Management via Deep Reinforcement Learning Heriberto Cuayáhuitl et al., 2015

• Generating Text with Deep Reinforcement Learning, Hongyu Guo, arXiv, 2015

• Deep Reinforcement Learning with a Natural Language Action Space, Ji He et al., arXiv, 2016.

• Language Understanding for Text-based Games using Deep Reinforcement Learning, Karthik Narasimhan arXiv, 2016

• Deep reinforcement learning for dialogue generation Jiwei Li et al., 2016

• End-to-end task-completion neural dialogue systems Xiujun Li et al., 2017

• Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning Paweł Budzianowski et al., 2017

• Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management Pei-Hao Su et al., 2017

• Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning Baolin Peng et al., 2017

• Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning Baolin Peng et al 2018

• Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog Jianping Zhang et al 2018

• Adversarial Learning of Task-Oriented Neural Dialog Models Bing Liu et al 2018.

• 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., arXiv 2015]

• Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation Iulian Vlad Serban et al., arXiv 2016s

• A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues Iulian Vlad Serban et al., 2016

• Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents Nabiha Asghar et al., 2016

• Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation

• Multi-turn Dialogue Response Generation in an Adversarial Learning Framework - Combining GAN with MLE in the objective.

• Improving Variational Encoder-Decoders in Dialogue Generation X Shen et al 2018.

• MojiTalk: Generating Emotional Responses at Scale Xianda Zhou et al 2018

• Exemplar Encoder-Decoder for Neural Conversation Generation Gaurav Pandey et al 2018

• Coupled Context Modeling for Deep Chit-Chat: Towards Conversations between Human and Computer(https://www.ruiyan.me/pubs/KDD2018Yan.pdf) Rui Yan et al KDD 2018.

• Variational Autoregressive Decoder for Neural Response Generation Jiachen Du et al 2018.

• Multi-view Response Selection for Human-Computer Conversation Xiangyang Zhou et al 2016

• Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots Yu Wu 2017

• Modeling multi-turn conversation with deep utterance aggregation Zhuosheng Zhang et al 2018

• Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network Xiangyang Zhang et al 2018.

• A Persona-Based Neural Conversation Model Jiwei Li et al, arXiv, 2016

• Conversational Contextual Cues: The Case of Personalization and History for Response Ranking Rami Al-Rfou et al., 2016

• Augmenting End-to-End Dialog Systems with Commonsense Knowledge Tom Young et al., 2017

• Topic Compositional Neural Language Model W Wang et al 2017

• Personalizing Dialogue Agents: I have a dog, do you have pets too? Zhang, Saizheng, et al., 2018

Some of the models are evaluated at CNN/Daily Mail and Children's Book Test (CBT) corpora.

• Teaching Machines to Read and Comprehend, Karl Moritz Hermann et al., arXiv, 2015.

  • Deep LSTM/Attentive Reader/Impatient Reader

• Text Understanding with the Attention Sum Reader Network, Rudolf Kadlec et al., arXiv, 2016.

• The Goldlocks Principle: Reading Children's Books With Explicit Memory Representations, Felix Hill., arXiv, 2016.

  • Memory Network

• End-To-End Memory Networks, Sainbayar Sukhbaatar et al., arXiv, 2015.

• Dynamic Entity Representation with Max-pooling Improves Machine Reading, Sosuke Kobayashi et al., arXiv, 2016.

• Gated-Attention Readers for Text Comprehension, Bhuwan Dhingra et al., arXiv, 2016.

• Iterative Alternating Neural Attention for Machine Reading, Alessandro Sordoni et al., arXiv, 2016.

• A Neural Network Approach to Context-Senstive Generation of Conversational Responses, Alessandro Sordoni et al, 2015

• Attention-over-Attention Neural Networks for Reading Comprehension Yiming Cui et al., arXiv 2016

• Hierarchical Recurrent Attention Network for Response Generation Chen Xing et al., 2017

• How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models Zhiliang Tian et al., 2017

• Chat More: Deepening and Widening the Chatting Topic via A Deep Model Wenjie Wang et al., 2018

• A Diversity-Promoting Objective Function for Neural Conversation Models Jiwei Li et al. 2016

• A Simple, Fast Diverse Decoding Algorithm for Neural Generation Jiwei Li et al., 2016

• Data Distillation for Controlling Specificity in Dialogue Generation Jiwei Li et al., 2017

• Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models Louis Shao et al., 2017

• Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders Tiancheng Zhao et al., 2017

• Latent variable dialogue models and their diversity Cao, Kris et al 2017

• DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder Xiaodong Gu et al 2018

• Towards a Neural Conversation Model with Diversity Net Using Determinantal Point Processes Yiping Song et al 2018

• Latent intention dialogue models Tsung-Hsien Wen et al., 2017

• Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation Tiancheng Zhao et al., 2018

• Learning to Control the Specificity in Neural Response Generation Ruqing Zhang et al 2018.

• Neural Information Retrieval: A Literature Review
• A Decomposable Attention Model for Natural Language Inference
• A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences
• Abstract Meaning Representation for Paraphrase Detection
• Bilateral Multi-Perspective Matching for Natural Language Sentences
• Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference
• Duplicate Question Identification by Integrating FrameNet with Neural Networks
• Dynamic Coattention Networks for Question Answering
• Injecting Relational Structural Representation in Neural Networks for Question Similarity
• Natural Language Inference over Interaction Space
• Neural Paraphrase Identification of Questions with Noisy Pretraining
• Paraphrase Detection Using Recursive Autoencoder
• Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information
• Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity

• RESCAL: A Three-Way Model for Collective Learning on Multi-Relational Data. Nickel Maximilian, Tresp Volker, Kriegel Hans-Peter. ICML 2011. paper OpenKE

• SE: Learning Structured Embeddings of Knowledge Bases. Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio. AAAI 2011. paper

• LFM: A Latent Factor Model for Highly Multi-relational Data. Rodolphe Jenatton, Nicolas L. Roux, Antoine Bordes, Guillaume R. Obozinski. NIPS 2012. paper

• NTN: Reasoning With Neural Tensor Networks for Knowledge Base Completion. Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng. NIPS 2013. paper

• TransE: Translating Embeddings for Modeling Multi-relational Data. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko. NIPS 2013. paper OpenKE

• TransM: Transition-based knowledge graph embedding with relational mapping properties. Fan M, Zhou Q, Chang E, et al. Proceedings of the 28th Pacific Asia Conference on Language. Information and Computing. 2014. paper

• TransH: Knowledge Graph Embedding by Translating on Hyperplanes. Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen. AAAI 2014. paper OpenKE

• TransR & CTransR: Learning Entity and Relation Embeddings for Knowledge Graph Completion. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. AAAI 2015. paper KB2E OpenKE

• TransD: Knowledge Graph Embedding via Dynamic Mapping Matrix. Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zhao. ACL 2015. paper KB2E OpenKE

• TransA: An Adaptive Approach for Knowledge Graph Embedding. Han Xiao, Minlie Huang, Hao Yu, Xiaoyan Zhu. arXiv 2015. paper

• KG2E: Learning to Represent Knowledge Graphs with Gaussian Embedding. Shizhu He, Kang Liu, Guoliang Ji and Jun Zhao. CIKM 2015. paper code

• DistMult: Embedding Entities and Relations for Learning and Inference in Knowledge Bases. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng. ICLR 2015. paper OpenKE

• PTransE: Modeling Relation Paths for Representation Learning of Knowledge Bases. Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu. EMNLP 2015. paper KB2E

• RTransE: Composing Relationships with Translations. Alberto García-Durán, Antoine Bordes, Nicolas Usunier. EMNLP 2015. paper

• ManifoldE: From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction. Han Xiao, Minlie Huang and Xiaoyan Zhu. IJCAI 2016. paper

• TransG: A Generative Mixture Model for Knowledge Graph Embedding. Han Xiao, Minlie Huang, Xiaoyan Zhu. ACL 2016. paper Embedding

• ComplEx: Complex Embeddings for Simple Link Prediction. Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier and Guillaume Bouchard. ICML 2016. paper code OpenKE

• ComplEx extension: Knowledge Graph Completion via Complex Tensor Factorization. Théo Trouillon, Christopher R. Dance, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard. JMLR 2017. paper code OpenKE

• HolE: Holographic Embeddings of Knowledge Graphs. Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio. AAAI 2016. paper code OpenKE

• KR-EAR: Knowledge Representation Learning with Entities, Attributes and Relations. Yankai Lin, Zhiyuan Liu, Maosong Sun. IJCAI 2016. paper KR-EAR

• TranSparse: Knowledge Graph Completion with Adaptive Sparse Transfer Matrix. Guoliang Ji, Kang Liu, Shizhu He, Jun Zhao. AAAI 2016. paper Fast-TransX

• TKRL: Representation Learning of Knowledge Graphs with Hierarchical Types. Ruobing Xie, Zhiyuan Liu, Maosong Sun. IJCAI 2016. paper TKRL

• TEKE: Text-Enhanced Representation Learning for Knowledge Graph. Zhigang Wang, Juan-Zi Li. IJCAI 2016. paper

• STransE: A Novel Embedding Model of Entities and Relationships in Knowledge Bases. Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu and Mark Johnson. NAACL-HLT 2016. paper STransE

• GAKE: Graph Aware Knowledge Embedding. Jun Feng, Minlie Huang, Yang Yang, Xiaoyan Zhu. COLING 2016. paper GAKE

• DKRL: Representation Learning of Knowledge Graphs with Entity Descriptions. Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, Maosong Sun. AAAI 2016. paper DKRL

• ProPPR: Learning First-Order Logic Embeddings via Matrix Factorization. William Yang Wang, William W. Cohen. IJCAI 2016. paper

• SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions. Han Xiao, Minlie Huang, Lian Meng, Xiaoyan Zhu. AAAI 2017. paper

• ProjE: Embedding Projection for Knowledge Graph Completion. Baoxu Shi, Tim Weninger. AAAI 2017. paper ProjE

• ANALOGY: Analogical Inference for Multi-relational Embeddings. Hanxiao Liu, Yuexin Wu, Yiming Yang. ICML 2017. paper knowledge-graph-embeddings

• IKRL: Image-embodied Knowledge Representation Learning. Ruobing Xie, Zhiyuan Liu, Tat-Seng Chua, Huan-Bo Luan, Maosong Sun. IJCAI 2017. paper IKRL

• ITransF: An Interpretable Knowledge Transfer Model for Knowledge Base Completion. Qizhe Xie, Xuezhe Ma, Zihang Dai, Eduard Hovy. ACL 2017. paper

• RUGE: Knowledge Graph Embedding with Iterative Guidance from Soft Rules. Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo. AAAI 2018. paper RUGE

• ConMask: Open-World Knowledge Graph Completion. Baoxu Shi, Tim Weninger. AAAI 2018. paper

• TorusE: Knowledge Graph Embedding on a Lie Group. Takuma Ebisu, Ryutaro Ichise. AAAI 2018. paper

• On Multi-Relational Link Prediction with Bilinear Models. Yanjie Wang, Rainer Gemulla, Hui Li. AAAI 2018. paper code

• Convolutional 2D Knowledge Graph Embeddings. Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel. AAAI 2018. paper ConvE

• Accurate Text-Enhanced Knowledge Graph Representation Learning. Bo An, Bo Chen, Xianpei Han, Le Sun. NAACL-HLT 2018. paper

• KBGAN: Adversarial Learning for Knowledge Graph Embeddings. Liwei Cai, William Yang Wang. NAACL-HLT 2018. paper KBGAN

• ConvKB: A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung .NAACL-HLT 2018. paper

• Open question answering with weakly supervised embedding models Bordes A, Weston J, Usunier N. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2014. paper
• Question answering with subgraph embeddings. Bordes A, Chopra S, Weston J. arXiv preprint arXiv:1406.3676, 2014. paper
• Variational Reasoning for Question Answering with KnowledgeGraph. Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J.Smola, and Le Song. AAAI 2018. paper
• Question Answering over Freebase via Attentive RNN withSimilarity Matrix based CNN[J]. Qu Y,Liu J, Kang L, et al. arXiv preprint arXiv:1804.03317, 2018.. paper
• Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. Das et al. ICLR 2018. paper

• Teaching machines to read and comprehend
• Attention-over-attention neural networks for reading comprehension
• Machine comprehension using match-lstm and answer pointer
• R-NET: MACHINE READING COMPREHENSION WITH SELF-MATCHING NETWORKS
• Bidirectional attention flow for machine comprehension
• Reading wikipedia to answer open-domain questions
• Towards Reading Comprehension for Long Documents !!#ff0000 new~!!

• Learning to Ask: Neural Question Generation for Reading Comprehension

• Smart Reply: Automated Response Suggestion for Email
• Distilling the Knowledge in a Neural Network

• https://github.com/snakeztc/NeuralDialogPapers