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Proceedings chapter
Open access
English

Getting across in medical communication: A corpus-based approach to analyze and improve the comprehensibility of machine translation

Presented at Winterthur (Switzerland), 29 June - 4 July 2020
PublisherWinterthur : ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Publication date2021
Abstract

In medical service contexts, especially in migrant- and refugee-receiving countries, the increasing need for inter-lingual communication and the higher cost of human translators has driven the development of machine translation technologies and tools. However, these tools' reliability and efficiency are questioned (Patil et al. 2014; Bouillon et al. 2017), which calls for objective evaluation to ensure and improve the quality of translation results. The present study aims to provide a data-driven empirical evaluation of the linguistic similarity of the French source speech data and their English translations produced by machine translation and identify the main areas in which MT output deviates from natural oral English. Using corpus statistical methods, the evaluation of machine translation outputs can provide concrete and objective feedback for future machine translation improvement. For future research, this model can be trained with larger datasets.

Keywords
  • Accessible communication
  • Audio description
  • Live subtitling
  • Rrespeaking
  • Easy-to-read
  • Web accessibility
  • Sign language
  • Leichte Sprache
  • 418.0087: Barrierefreie Kommunikation
Citation (ISO format)
LIU, Yanmeng, JI, Meng, BOUILLON, Pierrette. Getting across in medical communication: A corpus-based approach to analyze and improve the comprehensibility of machine translation. In: Proceedings of the 3rd Swiss conference on barrier-free communication (BfC 2020). Winterthur (Switzerland). Winterthur : ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2021. p. 39–45.
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Proceedings chapter (Published version)
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Identifiers
  • PID : unige:151997
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Technical informations

Creation05/29/2021 2:34:00 PM
First validation05/29/2021 2:34:00 PM
Update time03/16/2023 12:40:38 AM
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