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        <title>Improving Medical NLI Using Context-Aware Domain Knowledge</title>
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        <namePart type="given">Shaika</namePart>
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    <abstract>Domain knowledge is important to understand both the lexical and relational associations of words in natural language text, especially for domain-specific tasks like Natural Language Inference (NLI) in the medical domain, where due to the lack of a large annotated dataset such knowledge cannot be implicitly learned during training. However, because of the linguistic idiosyncrasies of clinical texts (e.g., shorthand jargon), solely relying on domain knowledge from an external knowledge base (e.g., UMLS) can lead to wrong inference predictions as it disregards contextual information and, hence, does not return the most relevant mapping. To remedy this, we devise a knowledge adaptive approach for medical NLI that encodes the premise/hypothesis texts by leveraging supplementary external knowledge, alongside the UMLS, based on the word contexts. By incorporating refined domain knowledge at both the lexical and relational levels through a multi-source attention mechanism, it is able to align the token-level interactions between the premise and hypothesis more effectively. Comprehensive experiments and case study on the recently released MedNLI dataset are conducted to validate the effectiveness of the proposed approach.</abstract>
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%0 Conference Proceedings
%T Improving Medical NLI Using Context-Aware Domain Knowledge
%A Chowdhury, Shaika
%A Yu, Philip
%A Luo, Yuan
%Y Gurevych, Iryna
%Y Apidianaki, Marianna
%Y Faruqui, Manaal
%S Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F chowdhury-etal-2020-improving
%X Domain knowledge is important to understand both the lexical and relational associations of words in natural language text, especially for domain-specific tasks like Natural Language Inference (NLI) in the medical domain, where due to the lack of a large annotated dataset such knowledge cannot be implicitly learned during training. However, because of the linguistic idiosyncrasies of clinical texts (e.g., shorthand jargon), solely relying on domain knowledge from an external knowledge base (e.g., UMLS) can lead to wrong inference predictions as it disregards contextual information and, hence, does not return the most relevant mapping. To remedy this, we devise a knowledge adaptive approach for medical NLI that encodes the premise/hypothesis texts by leveraging supplementary external knowledge, alongside the UMLS, based on the word contexts. By incorporating refined domain knowledge at both the lexical and relational levels through a multi-source attention mechanism, it is able to align the token-level interactions between the premise and hypothesis more effectively. Comprehensive experiments and case study on the recently released MedNLI dataset are conducted to validate the effectiveness of the proposed approach.
%U https://aclanthology.org/2020.starsem-1.1
%P 1-11

Markdown (Informal)

[Improving Medical NLI Using Context-Aware Domain Knowledge](https://aclanthology.org/2020.starsem-1.1) (Chowdhury et al., *SEM 2020)

• Improving Medical NLI Using Context-Aware Domain Knowledge (Chowdhury et al., *SEM 2020)

ACL

• Shaika Chowdhury, Philip Yu, and Yuan Luo. 2020. Improving Medical NLI Using Context-Aware Domain Knowledge. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pages 1–11, Barcelona, Spain (Online). Association for Computational Linguistics.