Lisibilité, fluidité et identification des erreurs lors de l'utilisation de la traduction automatique et de l'IA pour traduire des textes de recherche médicale
Résumé
Avec l’amélioration des algorithmes utilisés pour développer des outils de traduction automatique en ligne et l’importance croissante de l’IA, la qualité des résultats de ces outils suscite des inquiétudes croissantes. Par conséquent, nous avons cherché à évaluer la lisibilité des résultats de la traduction automatique et de l’IA pour la traduction de textes de recherche médicale de l’anglais vers le portugais européen. Au total, 16 articles scientifiques ont été collectés et deux traductions de chaque texte à traduire ont été obtenues à l’aide de Google Translate et de ChatGPT (n = 32). Plusieurs formules et indices de lisibilité ont été évalués, ainsi que la fluidité, la fidélité et la fréquence des erreurs linguistiques. Une analyse selon l’outil de traduction sélectionné n’a révélé aucune différence significative entre les résultats issus de Google Translate et de ChatGPT, en ce qui concerne l’analyse quantitative de la lisibilité, ce qui signifie que leurs performances étaient similaires malgré les problèmes perçus de fluidité et de fidélité. Cependant, les erreurs de discours étaient plus fréquentes dans les textes issus de ChatGPT (p < 0,001). Ces résultats sont encourageants et plaident en faveur de l’emploi d’outils de traduction automatique et d’IA pour traduire les textes de recherche médicale. Cependant, la relecture et la post-édition humaines ne peuvent être exclues, car les résultats des algorithmes dépendent largement de la complexité du texte.
Mots-clés
traduction automatique, intelligence artificielle, recherche médicale, lisibilité, qualité de la traductionRéférences
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