2019
ACL
ACL 2019
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
Abstract
AbstractThis paper presents the submissions by TeamDr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed specialized domains such as medicine.
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Trend Setter
— Natural Language Inference
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Hot Topic Early Bird
— domain knowledge
Authors
Topics
Machine Learning > Application Areas > Domain Adaptation
Natural Language Processing > Applications > Question Answering
Natural Language Processing > Resources & Methods > Natural Language Inference
Healthcare & Medicine > Clinical > Clinical NLP
Machine Learning > Learning Types > Transfer Learning
Natural Language Processing > Applications > Natural Language Inference