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

## TL;DR

This paper explores adapting state-of-the-art language models to the medical domain for textual inference and question entailment, demonstrating that domain-specific data augmentation improves performance in specialized medical NLP tasks.

## Contribution

The work introduces domain knowledge integration via data augmentation to enhance language models for medical textual inference and question entailment.

## Key findings

- Data augmentation with domain knowledge improves model accuracy.
- Strategies for generalizing language models to medical domain are effective.
- Results outperform baseline models on shared task metrics.

## 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.