多语言问答研究综述

doi:10.11896/jsjkx.210900003

摘要/Abstract

摘要： 多语言问答是自然语言处理领域的研究热点之一,其目的是给定不同语种的问题和文本,模型能够返回正确的答案。随着机器翻译技术的快速发展及多语言预训练技术在自然语言处理领域中的广泛应用,多语言问答也取得了较快的发展。文中首先系统地梳理了当前多语言问答方法的相关工作,并将多语言问答方法分为基于特征的方法、基于翻译的方法、基于预训练的方法和基于双重编码的方法,分别介绍了每类方法的使用和特点;然后系统地探讨了当前多语言问答任务的相关工作,将多语言问答任务分为基于文本的多语言问答任务和基于多模态的多语言问答任务,并分别给出每个多语言问答任务的基本定义;接着总结了这些任务中的数据集统计、评价指标,以及涉及的问答方法;最后展望了多语言问答的未来发展方向。

关键词: 多语言问答, 多语言预训练技术, 机器翻译, 基于多模态的多语言问答, 基于文本的多语言问答

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

中图分类号:

TP391

刘创, 熊德意. 多语言问答研究综述[J]. 计算机科学, 2022, 49(1): 65-72. https://doi.org/10.11896/jsjkx.210900003

LIU Chuang, XIONG De-yi. Survey of Multilingual Question Answering[J]. Computer Science, 2022, 49(1): 65-72. https://doi.org/10.11896/jsjkx.210900003

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