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<link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><h1 id="neural-network-dialog-system-papers">Neural Network Dialog System Papers</h1>
<p>A list of papers about creating dialog systems using deep nets! Please feel free to add an issue for suggesting missing <strong>good</strong> paper.</p>
<h1 id="bookmarks">Bookmarks</h1>
<ul>
<li><a href="#long-term-context">Long-term Context</a></li>
<li><a href="#task-bots">Task Bots</a></li>
<li><a href="#chat-bots">Chat Bots</a></li>
<li><a href="#reinforcement-learning">Reinforcement Learning</a></li>
</ul>
<h2 id="long-term-context">Long-term Context</h2>
<p>Mostly the models are evaluated at CNN/Daily Mail and Children's Book Test (CBT) corpora.</p>
<ul>
<li><a href="https://arxiv.org/abs/1506.03340" target="_blank" rel="external">Teaching Machines to Read and Comprehend</a>, Karl Moritz Hermann et al., <em>arXiv</em>, 2015.</li>
<li><p>Deep LSTM/Attentive Reader/Impatient Reader</p></li>
<li><p><a href="https://arxiv.org/abs/1603.01547" target="_blank" rel="external">Text Understanding with the Attention Sum Reader Network</a>, Rudolf Kadlec et al., <em>arXiv</em>, 2016.</p></li>
<li><a href="https://arxiv.org/abs/1511.02301" target="_blank" rel="external">The Goldlocks Principle: Reading Children's Books With Explicit Memory Representations</a>, Felix Hill., <em>arXiv</em>, 2016.</li>
<li><p>Memory Network</p></li>
<li><p><a href="http://arxiv.org/abs/1503.08895v5" target="_blank" rel="external">End-To-End Memory Networks</a>, Sainbayar Sukhbaatar et al., <em>arXiv</em>, 2015.</p></li>
<li><p><a href="http://www.cl.ecei.tohoku.ac.jp/publications/2016/kobayashi-dynamic-entity-naacl2016.pdf" target="_blank" rel="external">Dynamic Entity Representation with Max-pooling Improves Machine Reading</a>, Sosuke Kobayashi et al., <em>arXiv</em>, 2016.</p></li>
<li><p><a href="https://arxiv.org/abs/1606.01549" target="_blank" rel="external">Gated-Attention Readers for Text Comprehension</a>, Bhuwan Dhingra et al., <em>arXiv</em>, 2016.</p></li>
<li><p><a href="http://arxiv.org/abs/1606.02245v3" target="_blank" rel="external">Iterative Alternating Neural Attention for Machine Reading</a>, Alessandro Sordoni et al., <em>arXiv</em>, 2016.</p></li>
<li><p><a href="https://michaelauli.github.io/papers/chitchat.pdf" target="_blank" rel="external">A Neural Network Approach to Context-Senstive Generation of Conversational Responses</a>, Alessandro Sordoni et al, 2015</p></li>
<li><p><a href="https://arxiv.org/abs/1607.04423" target="_blank" rel="external">Attention-over-Attention Neural Networks for Reading Comprehension</a> Yiming Cui et al., <em>arXiv</em> 2016</p></li>
<li><p><a href="https://arxiv.org/pdf/1701.07149.pdf" target="_blank" rel="external">Hierarchical Recurrent Attention Network for Response Generation</a> Chen Xing et al., 2017</p></li>
<li><p><a href="http://www.aclweb.org/anthology/P17-2036" target="_blank" rel="external">How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models</a> Zhiliang Tian et al., 2017</p></li>
</ul>
<h2 id="task-bots">Task Bots</h2>
<ul>
<li><p><a href="http://arxiv.org/abs/1609.01462v1" target="_blank" rel="external">Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks</a>, Bing Liu, <em>arXiv</em>, 2016</p></li>
<li><p><a href="http://arxiv.org/abs/1609.01454v1" target="_blank" rel="external">Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling</a>, Bing Liu, <em>arXiv</em>, 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1604.04562" target="_blank" rel="external">A Network-based End-to-End Trainable Task-oriented Dialogue System</a> Tsung-Hsien Wen et al, 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1606.03352" target="_blank" rel="external">Conditional Generation and Snapshot Learning in Neural Dialogue Systems</a> Tsung-Hsien Wen et al, 2016</p></li>
<li><p><a href="http://media.wix.com/ugd/b6d786_137894b7b3a341a09ed0c0b45b46dbb6.pdf" target="_blank" rel="external">Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue</a> Ryan Lowe et al., 2016</p></li>
<li><p><a href="http://arxiv.org/pdf/1606.01269v1.pdf" target="_blank" rel="external">End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning</a>, Jason D. Williams et al., 2016</p></li>
<li><p><a href="https://arxiv.org/pdf/1606.02560v1.pdf" target="_blank" rel="external">Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning</a> Tiancheng Zhao et al., 2016</p></li>
<li><p><a href="http://arxiv.org/abs/1609.00777" target="_blank" rel="external">End-to-End Reinforcement Learning of Dialogue Agents for Information Access</a> Bhuwan Dhingra et al., 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1612.05688" target="_blank" rel="external">A User Simulator for Task-Completion Dialogues</a> Xinjun Li et al., 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1612.00913" target="_blank" rel="external">End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager</a> Xuesong Yang et al., 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1702.03274" target="_blank" rel="external">Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning</a> Jason D. Williams et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1704.07130" target="_blank" rel="external">Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings</a> He He et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1705.05414" target="_blank" rel="external">Key-Value Retrieval Networks for Task-Oriented Dialogue</a> M Eric et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1706.05125" target="_blank" rel="external">Deal or No Deal? End-to-End Learning for Negotiation Dialogues</a> Mike Lewis et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1706.08476" target="_blank" rel="external">Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability</a> Tiancheng Zhao et al., 2017</p></li>
<li><p><a href="https://arxiv.org/pdf/1708.05956.pdf" target="_blank" rel="external">An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog</a> Liu Bing et al., 2017</p></li>
<li><p><a href="https://arxiv.org/pdf/1706.06210.pdf" target="_blank" rel="external">Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning</a> Paweł et al., 2017</p></li>
<li><p><a href="http://workshop.colips.org/dstc6/papers/track1_paper02_wu.pdf" target="_blank" rel="external">End-to-End Recurrent Entity Network for Entity-Value Independent Goal-Oriented Dialog Learning</a> CS Wu et al 2017</p></li>
<li><p><a href="https://arxiv.org/pdf/1712.09943.pdf" target="_blank" rel="external">Toward Continual Learning for Conversational Agents</a> S Lee 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1801.04871" target="_blank" rel="external">Building a Conversational Agent Overnight with Dialogue Self-Play</a> Pararth Shah et al 2018</p></li>
</ul>
<h2 id="chat-bots">Chat Bots</h2>
<h3 id="general">General</h3>
<ul>
<li><p><a href="http://arxiv.org/abs/1506.05869" target="_blank" rel="external">A Neural Conversational Model</a> Oriol Vinyals et al., <em>arXiv</em> 2015]</p></li>
<li><p><a href="https://arxiv.org/pdf/1506.06714v1.pdf" target="_blank" rel="external">A Neural Network Approach to Context-Sensitive Generation of Conversational Responses∗</a> Alessandro Sordoni et al., <em>arXiv</em> 2015]</p></li>
<li><p><a href="https://arxiv.org/pdf/1606.00776v2.pdf" target="_blank" rel="external">Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation</a> Iulian Vlad Serban et al., <em>arXiv</em> 2016s</p></li>
<li><p><a href="https://arxiv.org/abs/1605.06069" target="_blank" rel="external">A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues</a> Iulian Vlad Serban et al., 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1612.03929" target="_blank" rel="external">Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents</a> Nabiha Asghar et al., 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1802.02032" target="_blank" rel="external">Improving Variational Encoder-Decoders in Dialogue Generation</a> X Shen et al 2018.</p></li>
</ul>
<h3 id="rich-dialog-context">Rich Dialog Context</h3>
<ul>
<li><p><a href="https://arxiv.org/abs/1603.06155" target="_blank" rel="external">A Persona-Based Neural Conversation Model</a> Jiwei Li et al, <em>arXiv</em>, 2016</p></li>
<li><p><a href="http://arxiv.org/pdf/1606.00372v1.pdf" target="_blank" rel="external">Conversational Contextual Cues: The Case of Personalization and History for Response Ranking</a> Rami Al-Rfou et al., 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1709.05453" target="_blank" rel="external">Augmenting End-to-End Dialog Systems with Commonsense Knowledge</a> Tom Young et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1712.09783" target="_blank" rel="external">Topic Compositional Neural Language Model</a> W Wang et al 2017</p></li>
</ul>
<h3 id="diversity">Diversity</h3>
<ul>
<li><p><a href="http://www.aclweb.org/anthology/N16-1014" target="_blank" rel="external">A Diversity-Promoting Objective Function for Neural Conversation Models</a> Jiwei Li et al. 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1611.08562" target="_blank" rel="external">A Simple, Fast Diverse Decoding Algorithm for Neural Generation</a> Jiwei Li et al., 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1702.06703" target="_blank" rel="external">Data Distillation for Controlling Specificity in Dialogue Generation</a> Jiwei Li et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1701.03185" target="_blank" rel="external">Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models</a> Louis Shao et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1703.10960" target="_blank" rel="external">Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders</a> Tiancheng Zhao et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1702.05962" target="_blank" rel="external">Latent variable dialogue models and their diversity</a> Cao, Kris et al 2017</p></li>
</ul>
<h3 id="reinforcement-learning-and-adversarial">Reinforcement Learning and Adversarial</h3>
<ul>
<li><p><a href="https://arxiv.org/abs/1606.01541" target="_blank" rel="external">Deep Reinforcement Learning for Dialogue Generation</a> Jiwei Li et al., <em>arXiv</em>, 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1701.06547" target="_blank" rel="external">Adversarial Learning for Neural Dialogue Generation</a> Jiwei Li et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1709.02349" target="_blank" rel="external">A deep reinforcement learning chatbot</a> Serban et al 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1711.10122" target="_blank" rel="external">End-to-end Adversarial Learning for Generative Conversational Agents</a> Ludwig, O. 2017.</p></li>
</ul>
<h2 id="reinforcement-learning">Reinforcement Learning</h2>
<ul>
<li><p><a href="https://arxiv.org/abs/1511.08099" target="_blank" rel="external">Strategic Dialogue Management via Deep Reinforcement Learning</a> Heriberto Cuayáhuitl et al., 2015</p></li>
<li><p><a href="http://arxiv.org/abs/1510.09202" target="_blank" rel="external">Generating Text with Deep Reinforcement Learning</a>, Hongyu Guo, <em>arXiv</em>, 2015</p></li>
<li><p><a href="http://arxiv.org/abs/1511.04636v5" target="_blank" rel="external">Deep Reinforcement Learning with a Natural Language Action Space</a>, Ji He et al., <em>arXiv</em>, 2016.</p></li>
<li><p><a href="https://arxiv.org/abs/1506.08941" target="_blank" rel="external">Language Understanding for Text-based Games using Deep Reinforcement Learning</a>, Karthik Narasimhan <em>arXiv</em>, 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1606.01541" target="_blank" rel="external">Deep reinforcement learning for dialogue generation</a> Jiwei Li et al., 2016</p></li>
<li><p><a href="https://arxiv.org/abs/1703.01008" target="_blank" rel="external">End-to-end task-completion neural dialogue systems</a> Xiujun Li et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1706.06210" target="_blank" rel="external">Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning</a> Paweł Budzianowski et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1707.00130" target="_blank" rel="external">Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management</a> Pei-Hao Su et al., 2017</p></li>
<li><p><a href="https://arxiv.org/abs/1704.03084" target="_blank" rel="external">Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning</a> Baolin Peng et al., 2017</p></li>
</ul>
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<div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#neural-network-dialog-system-papers"><span class="nav-number">1.</span> <span class="nav-text">Neural Network Dialog System Papers</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#bookmarks"><span class="nav-number">2.</span> <span class="nav-text">Bookmarks</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#long-term-context"><span class="nav-number">2.1.</span> <span class="nav-text">Long-term Context</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#task-bots"><span class="nav-number">2.2.</span> <span class="nav-text">Task Bots</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#chat-bots"><span class="nav-number">2.3.</span> <span class="nav-text">Chat Bots</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#general"><span class="nav-number">2.3.1.</span> <span class="nav-text">General</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#rich-dialog-context"><span class="nav-number">2.3.2.</span> <span class="nav-text">Rich Dialog Context</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#diversity"><span class="nav-number">2.3.3.</span> <span class="nav-text">Diversity</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#reinforcement-learning-and-adversarial"><span class="nav-number">2.3.4.</span> <span class="nav-text">Reinforcement Learning and Adversarial</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#reinforcement-learning"><span class="nav-number">2.4.</span> <span class="nav-text">Reinforcement Learning</span></a></li></ol></li></ol></div>
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