A Survey on Machine Reading Comprehension Systems. (19th November 2022)

• Record Type: Journal Article
• Authors: Baradaran, Razieh; Ghiasi, Razieh; Amirkhani, Hossein
• Is Part Of: Natural language engineering. Volume 28:Number 6(2022)
• Page Start: 683
• Page End: 732
• Publication Date: 2022-11-19
• Subjects: Natural Language Processing -- question answering -- Machine Reading Comprehension -- deep learning -- literature review; Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324921000395
• Languages: English
• ISSNs: 1351-3249

Abstract

Abstract: Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural Language Processing. The goal of this field is to develop systems for answering the questions regarding a given context. In this paper, we present a comprehensive survey on diverse aspects of MRC systems, including their approaches, structures, input/outputs, and research novelties. We illustrate the recent trends in this field based on a review of 241 papers published during 2016–2020. Our investigation demonstrated that the focus of research has changed in recent years from answer extraction to answer generation, from single- to multi-document reading comprehension, and from learning from scratch to using pre-trained word vectors. Moreover, we discuss the popular datasets and the evaluation metrics in this field. The paper ends with an investigation of the most-cited papers and their contributions.