Papers › ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, Benjamin Van Durme
We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at http://nlp.jhu.edu/record.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Common Sense Reasoning | ReCoRD | DocQA + ELMo | EM | 45.4 | #34 of 45 | Archive leaderboard | report |
| Common Sense Reasoning | ReCoRD | DocQA + ELMo | F1 | 46.7 | #34 of 45 | Archive leaderboard | report |
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