Papers
arxiv:1810.12885
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
Published on Oct 30, 2018
Abstract
A large-scale dataset, ReCoRD, highlights the significant gap between human and machine performance in commonsense reading comprehension.
AI-generated summary
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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