# IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering

## TL;DR

This paper reports on the IITP team's participation in the MEDIQA 2019 challenge, applying deep learning systems to natural language inference, question entailment, and medical question answering, achieving promising results.

## Contribution

The paper presents multiple deep learning systems for three MEDIQA tasks, demonstrating their effectiveness in medical NLP applications.

## Key findings

- Highest NLI accuracy: 81.8%
- Best RQE performance: 53.2%
- Top QA result: 71.7%

## Abstract

This paper presents the experiments accomplished as a part of our participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task. We participated in all the three tasks defined in this particular shared task. The tasks are viz. i. Natural Language Inference (NLI) ii. Recognizing Question Entailment(RQE) and their application in medical Question Answering (QA). We submitted runs using multiple deep learning based systems (runs) for each of these three tasks. We submitted five system results in each of the NLI and RQE tasks, and four system results for the QA task. The systems yield encouraging results in all three tasks. The highest performance obtained in NLI, RQE and QA tasks are 81.8%, 53.2%, and 71.7%, respectively.