Machine Reading Comprehension: a Literature Review
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Abstract (arXiv)
Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence. This article summarizes recent advances in MRC, mainly focusing on two aspects (i.e., corpus and techniques). The specific characteristics of various MRC corpus are listed and compared. The main ideas of some typical MRC techniques are also described.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jun 2019.
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Citing papers per year: 2021: 3; 2022: 1.
4 papers.
- 2021English Machine Reading Comprehension Datasets: A Surveycited by 26
- 2022Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoningcited by 8
- 2021Improving Question Answering Performance Using Knowledge Distillation and Active Learningcited by 6
- 2021Building a Question and Answer System for News Domaincited by 1
- 2022Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning2 May 2022
- 2021Improving Question Answering Performance Using Knowledge Distillation and Active Learning26 Sep 2021
- 2021Building a Question and Answer System for News Domain12 May 2021
- 2021English Machine Reading Comprehension Datasets: A Survey25 Jan 2021