Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
July 23, 2019 ยท Declared Dead ยท ๐ BioNLP@ACL
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Authors
Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary, James Route, Teruko Mitamura, Eric Nyberg
arXiv ID
1907.10136
Category
cs.CL: Computation & Language
Citations
6
Venue
BioNLP@ACL
Last Checked
5 months ago
Abstract
This paper presents the submissions by Team Dr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed by specialized domains such as medicine.
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