Inferential Question Answering
Inferential Question Answering is a computational framework within natural language processing that enables systems to derive answers by making implicit logical or semantic inferences beyond explicitly stated text, distinguishing it from literal or surface-level question answering methods.
36
Publications
2.3K
Citations
159
Authors
53
Institutions
Publications per year
2017–2026
18
Parents
36
Solving Quantitative Reasoning Problems with Language Models
Aitor Lewkowycz, Anders Andreassen, D. Dohan et al. · arXiv (Cornell University) · 2022 · 281 citations · Full text
Computing, Computer Science, Physics +15
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula et al. · 2019 · 277 citations · Full text
Question answering based on semantic structures
Srini Narayanan, Sanda M. Harabagiu · 2004 · 215 citations · Full text
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University of Washington
Seattle, United States
5 h-index
8 publications
666 citations
Allen Institute for Artificial Intelligence
Seattle, United States
3 h-index
13 publications
1.5K citations
University of Southern California
Los Angeles, United States
2 h-index
5 publications
86 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
310 citations
University of Houston
Houston, United States
1 h-index
2 publications
60 citations

Vassar College
Town of Poughkeepsie, United States
1 h-index
1 publications
10 citations
University of Pennsylvania
Philadelphia, United States
1 h-index
2 publications
54 citations
Amazon (United States)
Seattle, United States
1 h-index
5 publications
110 citations
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Repository · 1 publications · 281 citations
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Repository · 1 publications · 10 citations
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1 publications · 87 citations
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1 publications · 30 citations
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Repository · 1 publications · 27 citations
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