Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
This work addresses domain adaptation challenges in medical NLP, but it is incremental as it builds on prior methods.
The paper tackled adapting state-of-the-art language models to the medical domain for textual inference and question entailment, showing that data augmentation improved performance on a shared task.
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.