Neural Machine Reading Comprehension: Methods and Trends
It serves as a resource for researchers in natural language processing by summarizing advancements and identifying open issues in MRC, but it is incremental as it does not introduce new methods or results.
This paper addresses the lack of a comprehensive survey on neural machine reading comprehension (MRC) by providing a thorough review of existing methods, tasks, datasets, and emerging trends in the field.
Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years. Although research on MRC based on deep learning is flourishing, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the 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) the general architecture of neural MRC: the main modules and prevalent approaches to each; and (3) new trends: some emerging areas in neural MRC as well as the corresponding challenges. Finally, considering what has been achieved so far, the survey also envisages what the future may hold by discussing the open issues left to be addressed.