Context-to-Session Matching: Utilizing Whole Session for Response Selection in Information-Seeking Dialogue Systems
Zhenxin Fu: Peking University; Shaobo Cui: Alibaba Group; Mingyue Shang: Peking University; Feng Ji: DAMO Academy, Alibaba Group; Dongyan Zhao: Peking University; Haiqing Chen: Alibaba Group; Rui Yan: Peking University
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 context-to-response matching. Experiment results prove that our proposed context-to-session method outperforms the strong baselines significantly.
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