ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
This addresses the challenge of improving machine commonsense reasoning for NLP researchers, but is incremental as it focuses on dataset creation rather than a new method.
The authors tackled the problem of machine reading comprehension requiring commonsense reasoning by introducing a large-scale dataset called ReCoRD, and found that state-of-the-art MRC systems perform far behind human performance on this dataset.
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.