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Title

A Neural Machine Translation Approach to Translate Text to Pictographs in a Medical Speech Translation System - The BabelDr Use Case

Authors
Published in Kevin Duh, Francisco Guzman, Stephen Richardson. Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas. Orlando - September 12-16, 2022 - Orlando, USA: Association for Machine Translation in the Americas. 2022, p. 252-263
Abstract The use of images has been shown to positively affect patient comprehension in medical settings, in particular to deliver specific medical instructions. However, tools that automatically translate sentences into pictographs are still scarce due to the lack of resources. Previous studies have focused on the translation of sentences into pictographs by using WordNet combined with rule-based approaches and deep learning methods. In this work, we showed how we leveraged the BabelDr system, a speech to speech translator for medical triage, to build a speech to pictograph translator using UMLS and neural machine translation approaches. We showed that the translation from French sentences to a UMLS gloss can be viewed as a machine translation task and that a Multilingual Neural Machine Translation system achieved the best results.

Keywords Machine TranslationDeep LearningPictographUMLSBabelDRMedical Speech SystemHuman EvaluationAutomatic EvaluationAccesibility
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Research group TIM/ISSCO
Project
Swiss National Science Foundation: 10001FL_197864
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MUTAL, Jonathan David et al. A Neural Machine Translation Approach to Translate Text to Pictographs in a Medical Speech Translation System - The BabelDr Use Case. In: Kevin Duh, Francisco Guzman, Stephen Richardson (Ed.). Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas. Orlando. Orlando, USA : Association for Machine Translation in the Americas, 2022. p. 252-263. https://archive-ouverte.unige.ch/unige:163491

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Deposited on : 2022-09-22

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