2019
ACL
ACL 2019
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
Abstract
AbstractThis 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.
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Interdisciplinary Bridge
— Artificial Intelligence and Natural Language Processing
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Trend Setter
— Question Answering
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Keyword Pioneer
— medical domain
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Hot Topic Early Bird
— question answering
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Natural Language Processing > Applications > Question Answering
Natural Language Processing > Resources & Methods > Large Language Models
Natural Language Processing > Resources & Methods > Natural Language Inference
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Learning Types > Transfer Learning
Natural Language Processing > Applications > Natural Language Inference
Machine Learning > Learning Paradigms > Multi-Task Learning
Deep Learning > Learning Types > Transfer Learning
Natural Language Processing > Applications > Natural Language Understanding