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      Lingke: A Fine-grained Multi-turn Chatbot for Customer Service

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          Abstract

          Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.

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          Author and article information

          Journal
          10 August 2018
          Article
          1808.03430

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

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          Accepted by COLING 2018 demonstration paper
          cs.CL

          Theoretical computer science

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