Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 278-285.doi: 10.11896/jsjkx.210900250

• Artificial Intelligence • Previous Articles     Next Articles

Survey on Retrieval-based Chatbots

WU Yu1, LI Zhou-jun2   

  1. 1 Natural Language Computing Group,Microsoft Research Asia,Beijing 100080,China
    2 School of Computer Science and Engineering,Beihang University,Beijing 100191,China
  • Received:2020-03-20 Revised:2020-12-20 Online:2021-12-15 Published:2021-11-26
  • About author:WU Yu,born in 1992,Ph.D,senior researcher.His main research interests include natural language processing and spoken language processing.
    LI Zhou-jun,born in 1963,Ph.D,professor,is a member of China Computer Federation.His main research interests include data mining,natural language processing,network and information security.
  • Supported by:
    National Natural Science Foundation of China(U1636211,61672081) and Fund of the State Key Laboratory of Software Development Environment(SKLSDE-2021ZX-18).

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Abstract: With the rapid progress of natural language processing techniques and the massive accessible conversational data on Internet,non-tasked oriented dialogue systems,also referred to as Chatbots,have achieved great success,and drawn attention from both academia and industry.Currently,there are two lines in chatbots research,retrieval-based chatbots and generation-based chatbots.Due to the fluent responses and low latency,retrieval-based chatbots is a common method in practice.This paperfirst briefly introduces the research background, basic structure and component modules of retrieval-based chatbots,and then illustrates the constraints of the response selection module and related data set in details.Subsequently,we summarize recent popular techniques for response selection problem,including:statistic method,representation-based neural network method,interaction-based neural network method,and pre-training-based method.Finally,we pose the challenges of chatbots and outline promising directions as future work.

Key words: Chatbot, Natural language processing, Pre-training technology, Response selection, Text matching

CLC Number: 

  • TP391
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