@inproceedings{mutal-etal-2024-improving,
    title = "Improving Sign Language Production in the Healthcare Domain Using {UMLS} and Multi-task Learning",
    author = "Mutal, Jonathan David  and
      Rubino, Raphael  and
      Bouillon, Pierrette  and
      David, Bastien  and
      Gerlach, Johanna  and
      Strasly, Irene",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Thompson, Paul  and
      Ondov, Brian",
    booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cl4health-1.1",
    pages = "1--7",
    abstract = "This paper presents a study on Swiss-French sign language production in the medical domain. In emergency care settings, a lack of clear communication can interfere with accurate delivery of health related services. For patients communicating with sign language, equal access to healthcare remains an issue. While previous work has explored producing sign language gloss from a source text, we propose to extend this approach to produce a multichannel sign language output given a written French input. Furthermore, we extend our approach with a multi-task framework allowing us to include the Unified Medical Language System (UMLS) in our model. Results show that the introduction of UMLS in the training data improves model accuracy by 13.64 points.",
}

This paper presents a study on Swiss-French sign language production in the medical domain. In emergency care settings, a lack of clear communication can interfere with accurate delivery of health related services. For patients communicating with sign language, equal access to healthcare remains an issue. While previous work has explored producing sign language gloss from a source text, we propose to extend this approach to produce a multichannel sign language output given a written French input. Furthermore, we extend our approach with a multi-task framework allowing us to include the Unified Medical Language System (UMLS) in our model. Results show that the introduction of UMLS in the training data improves model accuracy by 13.64 points.
Mutal, Jonathan David Rubino, Raphael Bouillon, Pierrette David, Bastien Gerlach, Johanna Strasly, Irene
Improving Sign Language Production in the Healthcare Domain Using UMLS and Multi-task Learning