Patient Acceptance of Translation Technology for Medical Dialogues in Emergency Situations

Gerlach, Johanna; Bouillon, Pierrette; Troqe, Rovena; Halimi, Sonia; Spechbach, Hervé

doi:10.5040/9781350240117.ch-15

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…That evaluation will also allow us to validate the quality of the human and avatar videos and to compare patients' satisfaction level related to the diagnostic task. In another study, we showed that Arabic and Albanian patients have less confidence in their doctor when a speech translation system uses speech synthesis instead of human recording for translated output (Gerlach et al, 2023). It would be interesting to replicate that study for Deaf patients with human and avatar videos.…”

Section: Discussionmentioning

confidence: 86%

Healthcare Accessibility for the Deaf

Strasly,

Bouillon,

David

et al. 2023

Translation Technology in Accessible Health Communication

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Access to healthcare profoundly impacts the health and quality of life of Deaf people. Automatic translation tools are crucial in improving communication between Deaf patients and their healthcare providers. The aim of this chapter is to present the pipeline used to create the Swiss-French Sign Language (LSF-CH) version of BabelDr, a speech-enabled fixed-phrase translator that was initially conceived to improve communication in emergency settings between doctors and allophone patients (Bouillon et al, 2021). In order to do so, we start off by explaining how we ported BabelDr in LSF-CH using both human and avatar videos. We first describe the creation of a reference corpus consisting of video translations done by human translators, then we present a second corpus of videos generated with a virtual human. Finally, we relate the findings of a questionnaire on Deaf users' perspective on the use of signing avatars in the medical context. We showed that, although respondents prefer human videos, the use of automatic technologies associated with virtual characters is not without interest to the target audience and can be useful to them in the medical context.

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Section: Discussionmentioning

confidence: 86%

Healthcare Accessibility for the Deaf

Strasly,

Bouillon,

David

et al. 2023

Translation Technology in Accessible Health Communication

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“…Related to medical language, there are translation systems with pictographs, but they do not include WSD. This is the case of the French Text-to-Picto , but also for PictoDr, based on a neural translation approach using concepts, instead of words (Mutal et al, 2022;Gerlach et al, 2023). We therefore aim to assess the contribution of WSD in the context of specialized language for automatic translation into pictographs, an issue that has not yet been addressed in the literature.…”

Section: Related Workmentioning

confidence: 99%

Word Sense Disambiguation for Automatic Translation of Medical Dialogues into Pictographs

Norré,

Cardon,

Vandeghinste

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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Word sense disambiguation is an NLP task embedded in different applications. We propose to evaluate its contribution to the automatic translation of French texts into pictographs, in the context of communication between doctors and patients with an intellectual disability. Different general and/or medical language models (Word2Vec, fastText, CamemBERT, FlauBERT, DrBERT, and CamemBERT-bio) are tested in order to choose semantically correct pictographs leveraging the synsets in the French WordNets (WOLF and WoNeF). The results of our automatic evaluations show that our method based on Word2Vec and fastText significantly improves the precision of medical translations into pictographs. We also present an evaluation corpus adapted to this task.

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Patient Acceptance of Translation Technology for Medical Dialogues in Emergency Situations

Cited by 2 publications

References 16 publications

Healthcare Accessibility for the Deaf

Healthcare Accessibility for the Deaf

Word Sense Disambiguation for Automatic Translation of Medical Dialogues into Pictographs

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