Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (1): 65-72.doi: 10.11896/jsjkx.210900003

Special Issue: Natural Language Processing

• Multilingual Computing Advanced Technology • Previous Articles     Next Articles

Survey of Multilingual Question Answering

LIU Chuang, XIONG De-yi   

  1. College of Intelligence and Computing,Tianjin University,Tianjin 300350,China
  • Received:2021-07-14 Revised:2021-09-15 Online:2022-01-15 Published:2022-01-18
  • About author:LIU Chuang,born in 1990,Ph.D.His main research interests include question answering and commonsense reasoning.
    XIONG De-yi,born in 1979,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include machine translation,dialogue,and natural language generation.
  • Supported by:
    National Key Research and Development Program(2019QY1802).

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Abstract: Multilingual question answering is one of the research hotspots in the field of natural language processing,which aims to enable the model to return a correct answer based on understanding of the given questions and texts in different languages.With the rapid development of machine translation technology and the wide application of multilingual pre-training technology in the field of natural language processing,multilingual question answering has also achieved a relatively rapid development.This paper first systematically reviews the current work of multilingual question answering methods,and divides them into feature-based methods,translation-based methods,pre-training-based methods and dual encoding-based methods,and introduces the use and characteristics of each method respectively.Meanwhile,it also discusses the current work related to multilingual question answe-ring tasks,and divides them into text-based and multi-modal-based tasks and gives the basic definition of each one.Moreover,this paper summarizes the dataset statistics,evaluation metrics and multilingual question answering methods involved in these tasks.Finally,it proposes the future research prospect of multilingual question answering.

Key words: Machine translation, Multilingual pre-training techniques, Multilingual question answering, Multi-modal-based multilingual question answering, Text-based multilingual question answering

CLC Number: 

  • TP391
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