Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations

Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary, James Route, Teruko Mitamura, Eric Nyberg

[How to correct problems with metadata yourself]

Abstract
This paper presents the submissions by TeamDr.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 specialized domains such as medicine.
Anthology ID:
W19-5048
Volume:
Proceedings of the 18th BioNLP Workshop and Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
453–461
Language:
URL:
https://aclanthology.org/W19-5048
DOI:
10.18653/v1/W19-5048
Bibkey:
Cite (ACL):
Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary, James Route, Teruko Mitamura, and Eric Nyberg. 2019. Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 453–461, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations (Bannihatti Kumar et al., BioNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W19-5048.pdf
PDF Search