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Title:

Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

Abstract:

While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence in discourse-level decision-making.

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# Title Author Topic
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11 Survey on Evaluation Methods for Dialogue Systems Jan Deiru 1126
12 Recent Advances in Document Summarization Jin-ge Yao, Xiaojun Wan, Jianguo Xiao 1129
13 Machine Translation 3 (Neural), Dialogue Models Mohit Bansal 1142
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18 Modern Deep Learning Techniques Applied to Natural Language Processing Elvis Saravia, Soujanya Poria 1183
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25 Generating Sentences from a Continuous Space Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal ... 9999
26 A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models Yangfeng Ji, Gholamreza Haffari, Jacob Eisenstein 9999
27 Data Distillation for Controlling Specificity in Dialogue Generation Jiwei Li, Will Monroe, Dan Jurafsky 9999
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32 Neural Information Retrieval: A Literature Review Ye Zhang, Md Mustafizur Rahman, Alex Braylan, Brandon Dang, Heng-Lu... 1169
33 Neural Approaches to Conversational AI: Question Answering, Task-Oriented Dialogue and Chatbots: A Unified View Jianfeng Gao, Michel Galley, Lihong Li 1141
34 TOP RESEARCH PAPERS IN CONVERSATIONAL AI FOR CHATBOTS AND INTELLIGENT AGENTS Mariya Yao 1302
35 GANs for Simulation, Representation and Inference Ari Heljakka 713
36 An Attentional Neural Conversation Model with Improved Specificity Kaisheng Yao, Baolin Peng, Geoffrey Zweig, Kam-Fai Wong 9999
37 Papers With Code : Dialogue Generation None
38 Language+Vision Mohit Bansal 1338
39 Encoder-Decoder Neural Networks Nal Kalchbrenner 1155
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41 A Survey of the Usages of Deep Learning in Natural Language Processing Daniel W. Otter, Julian R. Medina, Jugal K. Kalita 1181
42 LSTM based Conversation Models Yi Luan, Yangfeng Ji, Mari Ostendorf 9999
43 The Handbook of Computational Linguistics and Natural Language Processing "Alexander Clark, Chris Fox, and Shalom Lappin" 1557
44 Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning Tiancheng Zhao, Maxine Eskenazi 9999
45 Neural Belief Tracker: Data-Driven Dialogue State Tracking Nikola Mrkšic?, Diarmuid Ó Séaghdha, Tsung-Hsien Wen, Blaise Thomso... 9999
46 Neural Information Retrieval: At the End of the Early Years Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizu... 1183
47 A Survey on Semantic Parsing Aishwarya Kamath and Rajarshi Das 1218
48 Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation Albert Gatt, Emiel Krahmer 1136
49 Summaries and notes on Deep Learning research papers Denny Britz 1183
50 A Survey of Text Summarization Techniques Ani Nenkova, Kathleen McKeown 1129