Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 286-296.doi: 10.11896/jsjkx.210100209

• Artificial Intelligence • Previous Articles     Next Articles

Review on Interactive Question Answering Techniques Based on Deep Learning

HUANG Xin1, LEI Gang1, CAO Yuan-long1, LU Ming-ming2   

  1. 1 School of Software,Jiangxi Normal University,Nanchang 330022,China
    2 School of Electronics and Information Engineering,Tongji University,Shanghai 200092,China
  • Received:2021-01-27 Revised:2021-04-24 Online:2021-12-15 Published:2021-11-26
  • About author:HUANG Xin,born in 1984,lecturer,is a member of China Computer Federation.His main research interests include machine learning,natural language processing and biological information.
    LEI Gang,born in 1974,associate professor,master tutor.His main research interests include natural language processing,knowledge discovery and machine learning.
  • Supported by:
    Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ200318)and National Natural Science Foundation of China(61962026).

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Abstract: Compared to the traditional question answering(QA),interactive question answering(IQA) considers dialogue context and background information,which brings new challenges to understand user input and reason answers.First of all,user input is not only limited to questions,but can also be utterances that inform the details of the question and give feedback on whether the answer is feasible or not.Therefore,it is necessary to understand the intent of each utterance in the dialogue.Secondly,IQA allows multiple characters to discuss a question at the same time,generating personalized answers.So,it is necessary to understand different characters and identify them from each other.Thirdly,when IQA revolves around a background document,it is necessary to understand this document and extract answers from it.This paper reviews recent development in three subareas:IQA without background,IQA with background,and the application of transfer learning in IQA,and finally discusses the future perspective of interactive question answering.

Key words: Background, Dialogue system, Interactive question answering, Pre-trained models

CLC Number: 

  • TP391
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