# Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model

## TL;DR

This paper enhances natural language inference in the clinical domain by applying pre-trained language models and transfer learning, achieving high accuracy despite domain-specific language challenges.

## Contribution

It introduces the use of large-scale pre-trained models for clinical NLP tasks, demonstrating improved performance over traditional methods.

## Key findings

- Achieved 90.6% accuracy in clinical NLI task.
- Showed the effectiveness of transfer learning in medical NLP.
- Provided analysis to guide model component selection.

## Abstract

While deep learning techniques have shown promising results in many natural language processing (NLP) tasks, it has not been widely applied to the clinical domain. The lack of large datasets and the pervasive use of domain-specific language (i.e. abbreviations and acronyms) in the clinical domain causes slower progress in NLP tasks than that of the general NLP tasks. To fill this gap, we employ word/subword-level based models that adopt large-scale data-driven methods such as pre-trained language models and transfer learning in analyzing text for the clinical domain. Empirical results demonstrate the superiority of the proposed methods by achieving 90.6% accuracy in medical domain natural language inference task. Furthermore, we inspect the independent strengths of the proposed approaches in quantitative and qualitative manners. This analysis will help researchers to select necessary components in building models for the medical domain.