Evaluation of Machine Translation Methods applied to Medical Terminologies

Konstantinos Skianis, Yann Briand, Florent Desgrippes


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.
Anthology ID:
2020.louhi-1.7
Volume:
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
Month:
November
Year:
2020
Address:
Online
Editors:
Eben Holderness, Antonio Jimeno Yepes, Alberto Lavelli, Anne-Lyse Minard, James Pustejovsky, Fabio Rinaldi
Venue:
Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
59–69
Language:
URL:
https://aclanthology.org/2020.louhi-1.7
DOI:
10.18653/v1/2020.louhi-1.7
Bibkey:
Cite (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.
Cite (Informal):
Evaluation of Machine Translation Methods applied to Medical Terminologies (Skianis et al., Louhi 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.louhi-1.7.pdf
Video:
 https://slideslive.com/38940042
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