Machine Reading Comprehension
Machine Reading Comprehension is a computational task within natural language understanding that requires a machine to automatically answer questions based on a given textual passage, thereby demonstrating comprehension through accurate and contextually grounded responses.
267
Publications
27.9K
Citations
863
Authors
207
Institutions
Publications per year
2017–2026
251
267
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev et al. · 2016 · 6.3K citations · Full text
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette et al. · arXiv (Cornell University) · 2015 · 1.5K citations · Full text
Reading Wikipedia to Answer Open-Domain Questions
Danqi Chen, Adam Fisch, Jason Weston et al. · 2017 · 1.4K citations · Full text
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863
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207
Microsoft (United States)
Redmond, United States
12 h-index
37 publications
6.8K citations

Shanghai Jiao Tong University
Shanghai, China
12 h-index
41 publications
4.2K citations
Baidu (China)
Beijing, China
11 h-index
52 publications
5.3K citations

Harbin Institute of Technology
Harbin, China
11 h-index
42 publications
1.4K citations

Peking University
Beijing, China
9 h-index
23 publications
1.4K citations
Microsoft Research Asia (China)
Beijing, China
9 h-index
32 publications
2.7K citations
University of Washington
Seattle, United States
9 h-index
32 publications
1.5K citations
Tsinghua University
Beijing, China
8 h-index
23 publications
1.1K citations
1–8 of 207
Repository · 72 publications · 6.6K citations
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Conference · 20 publications · 1.6K citations
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TASLP · 5 publications · 99 citations
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3 publications · 62 citations
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3 publications · 43 citations
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