计算机工程与应用 ›› 2021, Vol. 57 ›› Issue (12): 25-36.DOI: 10.3778/j.issn.1002-8331.2102-0038

• 热点与综述 • 上一篇    下一篇

抽取式机器阅读理解研究综述

包玥,李艳玲,林民   

  1. 内蒙古师范大学 计算机科学技术学院,呼和浩特 010022
  • 出版日期:2021-06-15 发布日期:2021-06-10

Review of Extractive Machine Reading Comprehension

BAO Yue, LI Yanling, LIN Min   

  1. College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China
  • Online:2021-06-15 Published:2021-06-10

摘要:

机器阅读理解要求机器能够理解自然语言文本并回答相关问题,是自然语言处理领域的核心技术,也是自然语言处理领域最具挑战性的任务之一。抽取式机器阅读理解是机器阅读理解任务中一个重要的分支,因其更贴合实际情况,更能够反映机器的理解能力,成为当前学术界和工业界的研究热点。对抽取式机器阅读理解从以下四个方面进行了全面地综述:介绍了机器阅读理解任务及其发展历程;介绍了抽取式机器阅读理解任务以及其现阶段存在的难点;对抽取式机器阅读理解任务的主要数据集及方法进行了梳理总结;讨论了抽取式机器阅读理解的未来发展方向。

关键词: 抽取式机器阅读理解, 自然语言处理, 深度学习, 迁移学习, 注意力机制

Abstract:

Machine reading comprehension requires machines to understand natural language texts and answer related questions, which is the core technology in the field of natural language processing and one of the most challenging tasks in the field of natural language processing. Extractive machine reading comprehension is an important branch of machine reading comprehension task. Because it is more suitable for the actual situation and can reflect the understanding ability of the machine, it has become a research hotspot in the current academic and industrial circles. This paper makes a comprehensive review of extractive machine reading comprehension from four aspects, first of all, the paper introduces the task of machine reading comprehension and its development process. Secondly, it describes the task of extractive machine reading comprehension and its difficulties at present. Then, the main data sets and methods of the extractive machine reading comprehension task are summarized. Finally, the future development direction of extractive machine reading comprehension is discussed.

Key words: extractive machine reading comprehension, natural language processing, deep learning, transfer learning, attention mechanism