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      A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

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          Abstract

          We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.

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          Author and article information

          Journal
          22 June 2015
          Article
          1506.06714

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

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          A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell, J.-Y. Nie, J. Gao, B. Dolan. 2015. A Neural Network Approach to Context-Sensitive Generation of Conversational Responses. In Proc. of NAACL-HLT. Pages 196-205
          cs.CL cs.AI cs.LG cs.NE

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