[KDD 2020] Context-to-Session Matching: Utilizing Whole Session for Response Selection in Information-Seeking Dialogue Systems

[KDD 2020] Context-to-Session Matching: Utilizing Whole Session for Response Selection in Information-Seeking Dialogue Systems

Dec 16, 2020
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We study the retrieval-based multi-turn information-seeking dialogue systems, which are widely used in many scenarios. Most of,the previous works select the response according to the matching,degree between the query’s context and the candidate responses.,Though great progress has been made, existing works ignore the,contexts of the responses, which could provide rich information,for selecting the most appropriate response. The more similar the,query’s context and certain response’s context are, the more likely,they are to indicate the same question, and thus, the more likely,this response is to answer the query. In this paper, we consider the,response and its context as a whole,session,and explore the task of,matching the query’s context with the sessions. More specifically,,we propose to match between the query’s context and response’s,context and integrate the context-to-context matching with contextto-response matching. Experiment results prove that our proposed,context-to-session method outperforms the strong baselines significantly.

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