Using Translation Techniques to Characterize MT Outputs
Álvarez Vidal, Sergi (Universitat Autònoma de Barcelona)
Olalla-Soler, Christian (Universitat Autònoma de Barcelona)
Sánchez-Gijón, Pilar (Universitat Autònoma de Barcelona)

Date:	2025
Description:	9 pàg.
Abstract:	While current NMT and GPT models improve fluency and context awareness, they struggle with creative texts, where figurative language and stylistic choices are crucial. Current evaluation methods fail to capture these nuances, which requires a more descriptive approach. We propose a taxonomy based on translation techniques to assess machine-generated translations more comprehensively. The pilot study we conducted comparing human machine-produced translations reveals that human translations employ a wider range of techniques, enhancing naturalness and cultural adaptation. NMT and GPT models, even with prompting, tend to simplify content and introduce accuracy errors. Our findings highlight the need for refined frameworks that consider stylistic and contextual accuracy, ultimately bridging the gap between human and machine translation performance.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució i la comunicació pública de l'obra, sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Capítol de llibre ; recerca ; Versió publicada
Subject:	Neural machine translation (NMT) ; Generative pre-trained transformer (GPT) ; Translation techniques ; Machine-generated translations ; Human translations ; Comparative study
Published in:	Proceedings of Machine Translation Summit XX, Vol. 1 (2025) , p. 619-627, ISBN 978-2-9701897-0-1

9 p, 228.4 KB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Arts and Humanities > Grup d'estudi de la literacitat en l’ensenyament i l’aprenentatge de segones llengües i traducció (GELEA2LT)
Books and collections > Book chapters