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      Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots

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          Abstract

          We study response selection for multi-turn conversation in retrieval based chatbots. Existing works either ignores relationships among utterances, or misses important information in context when matching a response with a highly abstract context vector finally. We propose a new session based matching model to address both problems. The model first matches a response with each utterance on multiple granularities, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models the relationships among the utterances. The final matching score is calculated with the hidden states of the RNN. Empirical study on two public data sets shows that our model can significantly outperform the state-of-the-art methods for response selection in multi-turn conversation.

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          Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

           Rui Yan,  Yiping Song,  Hua Wu (2016)
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            The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management

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              Author and article information

              Journal
              2016-12-05
              Article
              1612.01627

              http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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              cs.CL

              Theoretical computer science

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