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.
@article{arxiv.1907.10136,
title = {Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations},
author = {Vinayshekhar Bannihatti Kumar and Ashwin Srinivasan and Aditi Chaudhary and James Route and Teruko Mitamura and Eric Nyberg},
journal= {arXiv preprint arXiv:1907.10136},
year = {2019}
}
Comments
Accepted in ACL challenge MediQA as part of the BioNLP workshop