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


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 Medium Score
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12 Papers With Code : Dialogue Generation None
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32 The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems Ryan Lowe, Nissan Pow, Iulian Serban, Joelle Pineau 9999
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36 Neural Text Generation: A Practical Guide Ziang Xie 1135
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38 Neural Text Generation: {A} Practical Guide Ziang Xie 1033
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44 A Survey on Neural Network-Based Summarization Methods Yue Dong 1198
45 Language to Logical Form with Neural Attention Li Dong, Mirella Lapata 9999
46 Learning to Generate Textual Data Guillaume Bouchard, Pontus Stenetorp, Sebastian Riedel 9999
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