CliCR: a Dataset of Clinical Case Reports for Machine Reading Comprehension

Simon Šuster, Walter Daelemans


Abstract
We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply several baselines and state-of-the-art neural readers to the dataset, and observe a considerable gap in performance (20% F1) between the best human and machine readers. We analyze the skills required for successful answering and show how reader performance varies depending on the applicable skills. We find that inferences using domain knowledge and object tracking are the most frequently required skills, and that recognizing omitted information and spatio-temporal reasoning are the most difficult for the machines.
Anthology ID:
N18-1140
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1551–1563
Language:
URL:
https://www.aclweb.org/anthology/N18-1140
DOI:
10.18653/v1/N18-1140
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1140.pdf