# UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference

## TL;DR

This paper compares different representation methods—BERT, ESP, Cui2Vec—for medical natural language inference, analyzing their performance and internal representations on the MedNLI task to understand their effectiveness in semantic understanding.

## Contribution

It provides a comparative analysis of three representation methods for medical NLP, highlighting their strengths and differences in a challenging inference task.

## Key findings

- BERT outperforms other methods in accuracy.
- Semantic understanding varies significantly across methods.
- Internal representations reveal different semantic capture capabilities.

## Abstract

Recent advances in distributed language modeling have led to large performance increases on a variety of natural language processing (NLP) tasks. However, it is not well understood how these methods may be augmented by knowledge-based approaches. This paper compares the performance and internal representation of an Enhanced Sequential Inference Model (ESIM) between three experimental conditions based on the representation method: Bidirectional Encoder Representations from Transformers (BERT), Embeddings of Semantic Predications (ESP), or Cui2Vec. The methods were evaluated on the Medical Natural Language Inference (MedNLI) subtask of the MEDIQA 2019 shared task. This task relied heavily on semantic understanding and thus served as a suitable evaluation set for the comparison of these representation methods.