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A Survey on Machine Reading Comprehension Systems

Published online by Cambridge University Press:  19 January 2022

Razieh Baradaran Open the ORCID record for Razieh Baradaran [Opens in a new window] ,
Razieh Ghiasi  and
Hossein Amirkhani
Razieh Baradaran
Affiliation:
Computer and Information Technology Department, University of Qom, Qom, Iran
Razieh Ghiasi
Affiliation:
Computer and Information Technology Department, University of Qom, Qom, Iran
Hossein Amirkhani*
Affiliation:
Computer and Information Technology Department, University of Qom, Qom, Iran
*
*Corresponding author. E-mail: amirkhani@qom.ac.ir
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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.

Keywords

Natural Language Processingquestion answeringMachine Reading Comprehensiondeep learningliterature review
Type
Survey Paper
Information
Natural Language Engineering , Volume 28 , Issue 6 , November 2022 , pp. 683 - 732
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

**

Equal contribution

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