2019
ACL
ACL 2019
MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.
Abstract
AbstractIn this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge. Bio-MTDNN utilizes “transfer learning” based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Trend Setter
— Natural Language Inference
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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
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Keyword Pioneer
— biomedical language understanding
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Hot Topic Early Bird
— knowledge integration
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Application Areas > Domain Adaptation
Natural Language Processing > Resources & Methods > Natural Language Inference
Healthcare & Medicine > Clinical > Clinical NLP
Machine Learning > Learning Paradigms > Transfer Learning
Natural Language Processing > Applications > Natural Language Inference
Machine Learning > Learning Paradigms > Multi-Task Learning