@inproceedings{alvarez-vidal-etal-2025-using,
title = "Using Translation Techniques to Characterize {MT} Outputs",
author = "Alvarez-Vidal, Sergi and
Campo, Maria Do and
Olalla-Soler, Christian and
S{\'a}nchez-Gij{\'o}n, Pilar",
editor = "Bouillon, Pierrette and
Gerlach, Johanna and
Girletti, Sabrina and
Volkart, Lise and
Rubino, Raphael and
Sennrich, Rico and
Farinha, Ana C. and
Gaido, Marco and
Daems, Joke and
Kenny, Dorothy and
Moniz, Helena and
Szoc, Sara",
booktitle = "Proceedings of Machine Translation Summit XX: Volume 1",
month = jun,
year = "2025",
address = "Geneva, Switzerland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2025.mtsummit-1.47/",
pages = "619--627",
ISBN = "978-2-9701897-0-1",
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."
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%0 Conference Proceedings
%T Using Translation Techniques to Characterize MT Outputs
%A Alvarez-Vidal, Sergi
%A Campo, Maria Do
%A Olalla-Soler, Christian
%A Sánchez-Gijón, Pilar
%Y Bouillon, Pierrette
%Y Gerlach, Johanna
%Y Girletti, Sabrina
%Y Volkart, Lise
%Y Rubino, Raphael
%Y Sennrich, Rico
%Y Farinha, Ana C.
%Y Gaido, Marco
%Y Daems, Joke
%Y Kenny, Dorothy
%Y Moniz, Helena
%Y Szoc, Sara
%S Proceedings of Machine Translation Summit XX: Volume 1
%D 2025
%8 June
%I European Association for Machine Translation
%C Geneva, Switzerland
%@ 978-2-9701897-0-1
%F alvarez-vidal-etal-2025-using
%X 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.
%U https://aclanthology.org/2025.mtsummit-1.47/
%P 619-627
Markdown (Informal)
[Using Translation Techniques to Characterize MT Outputs](https://aclanthology.org/2025.mtsummit-1.47/) (Alvarez-Vidal et al., MTSummit 2025)
ACL
- Sergi Alvarez-Vidal, Maria Do Campo, Christian Olalla-Soler, and Pilar Sánchez-Gijón. 2025. Using Translation Techniques to Characterize MT Outputs. In Proceedings of Machine Translation Summit XX: Volume 1, pages 619–627, Geneva, Switzerland. European Association for Machine Translation.