DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
This work addresses the problem of improving performance on medical NLP tasks for researchers and practitioners, but it is incremental as it builds on existing transfer learning methods.
The paper tackled natural language understanding in the medical domain by using a multi-source transfer learning approach, achieving first place on the QA task in the MEDIQA-2019 competition.
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.