Neural Machine Reading Comprehension: Methods and Trends
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Natural Language Processing Techniques · Text Readability and Simplification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Science and Technology on Information Systems Engineering Laboratory, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
References (100+)
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