  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.
