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        <title>Lessons from Natural Language Inference in the Clinical Domain</title>
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        <namePart type="given">Alexey</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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    <abstract>State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.</abstract>
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%0 Conference Proceedings
%T Lessons from Natural Language Inference in the Clinical Domain
%A Romanov, Alexey
%A Shivade, Chaitanya
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F romanov-shivade-2018-lessons
%X State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.
%R 10.18653/v1/D18-1187
%U https://aclanthology.org/D18-1187
%U https://doi.org/10.18653/v1/D18-1187
%P 1586-1596

Markdown (Informal)

[Lessons from Natural Language Inference in the Clinical Domain](https://aclanthology.org/D18-1187) (Romanov & Shivade, EMNLP 2018)

• Lessons from Natural Language Inference in the Clinical Domain (Romanov & Shivade, EMNLP 2018)

ACL

• Alexey Romanov and Chaitanya Shivade. 2018. Lessons from Natural Language Inference in the Clinical Domain. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1586–1596, Brussels, Belgium. Association for Computational Linguistics.