@inproceedings{skianis-etal-2020-evaluation,
    title = "Evaluation of Machine Translation Methods applied to Medical Terminologies",
    author = "Skianis, Konstantinos  and
      Briand, Yann  and
      Desgrippes, Florent",
    editor = "Holderness, Eben  and
      Jimeno Yepes, Antonio  and
      Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Pustejovsky, James  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.louhi-1.7/",
    doi = "10.18653/v1/2020.louhi-1.7",
    pages = "59--69",
    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."
}