# DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain

arXiv: https://arxiv.org/abs/1906.04382 · HTML: https://mlsanity.com/paper/1906.04382/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning](https://mlsanity.com/paper/2205.08124/) (2022)
- [Doctor Recommendation in Online Health Forums via Expertise Learning](https://mlsanity.com/paper/2203.02932/) (2022)

## Abstract (arXiv)

This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task.