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Proceedings chapter
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English

Improving Sign Language Production in the Healthcare Domain Using UMLS and Multi-Task Learning

Published inProceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024, Editors Dina Demner-Fushman, Sophia Ananiadou, Paul Thompson, Brian Ondov, p. 1-7
Presented at Torino, 20 May, 2024
PublisherTorino, Italia : ELRA
Publication date2024-05-20
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.

eng
Keywords
  • Sign language production
  • UMLS
  • Multi-task learning
  • Medical dialog
Research group
Funding
  • French National Research Agency (ANR) - PRojection of the Oral language to PICTOgraphic units [ANR-20-CE93-0005]
Citation (ISO format)
MUTAL, Jonathan David et al. Improving Sign Language Production in the Healthcare Domain Using UMLS and Multi-Task Learning. In: Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024. Torino. Torino, Italia : ELRA, 2024. p. 1–7.
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Proceedings chapter (Published version)
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Identifiers
  • PID : unige:177411
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