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        <title>Evaluation of Machine Translation Methods applied to Medical Terminologies</title>
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            <title>Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis</title>
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        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Jimeno Yepes</namePart>
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            <namePart type="given">Anne-Lyse</namePart>
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    <abstract>Medical terminologies resources and standards play vital roles in clinical data exchanges, enabling significantly the services’ interoperability within healthcare national information networks. Health and medical science are constantly evolving causing requirements to advance the terminologies editions. In this paper, we present our evaluation work of the latest machine translation techniques addressing medical terminologies. Experiments have been conducted leveraging selected statistical and neural machine translation methods. The devised procedure is tested on a validated sample of ICD-11 and ICF terminologies from English to French with promising results.</abstract>
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%0 Conference Proceedings
%T Evaluation of Machine Translation Methods applied to Medical Terminologies
%A Skianis, Konstantinos
%A Briand, Yann
%A Desgrippes, Florent
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F skianis-etal-2020-evaluation
%X Medical terminologies resources and standards play vital roles in clinical data exchanges, enabling significantly the services’ interoperability within healthcare national information networks. Health and medical science are constantly evolving causing requirements to advance the terminologies editions. In this paper, we present our evaluation work of the latest machine translation techniques addressing medical terminologies. Experiments have been conducted leveraging selected statistical and neural machine translation methods. The devised procedure is tested on a validated sample of ICD-11 and ICF terminologies from English to French with promising results.
%R 10.18653/v1/2020.louhi-1.7
%U https://aclanthology.org/2020.louhi-1.7
%U https://doi.org/10.18653/v1/2020.louhi-1.7
%P 59-69

Markdown (Informal)

[Evaluation of Machine Translation Methods applied to Medical Terminologies](https://aclanthology.org/2020.louhi-1.7) (Skianis et al., Louhi 2020)

• Evaluation of Machine Translation Methods applied to Medical Terminologies (Skianis et al., Louhi 2020)

ACL

• Konstantinos Skianis, Yann Briand, and Florent Desgrippes. 2020. Evaluation of Machine Translation Methods applied to Medical Terminologies. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, pages 59–69, Online. Association for Computational Linguistics.