Machine Reading Comprehension (MRC), which requires the machine to answer questions based on the given context, has gained increasingly wide attention with the incorporation of various deep learning techniques over the past few years. Although the research of MRC based on deep learning is flourishing, there remains a lack of a comprehensive survey to summarize existing approaches and recent trends, which motivates our work presented in this article. Specifically, we give a thorough review of this research field, covering different aspects including (1) typical MRC tasks: their definitions, differences and representative datasets; (2) general architecture of neural MRC: the main modules and prevalent approaches to each of them; and (3) new trends: some emerging focuses in neural MRC as well as the corresponding challenges. Last but not least, in retrospect of what has been achieved so far, the survey also envisages what the future may hold by discussing the open issues left to be addressed.
翻译:机器阅读理解(MRC)要求机器根据特定背景回答问题,在过去几年中,随着各种深层学习技术的采用,这种理解日益引起人们的广泛关注。虽然基于深层学习的MRC研究正在蓬勃发展,但目前仍缺乏全面调查来总结现有办法和最新趋势,这促使我们在本条中介绍我们的工作。具体地说,我们对这一研究领域进行彻底审查,涉及不同方面,包括:(1) 典型的MRC任务:其定义、差异和代表性数据集;(2) 神经MRC的一般结构:每个主要模块和通用方法;(3) 新趋势:一些新出现的焦点在神经MRC以及相应的挑战中。最后但同样重要的是,回顾迄今取得的成就,调查还设想了讨论有待解决的未决问题可能给未来带来什么。