@inproceedings{mihalache-salinas-2026-meaning,
title = "Meaning-Making Process and Error Dynamics in {C}hat{GPT}-Mediated Translation",
author = "Mihalache, Iulia and
Salinas, Mar{\'i}a-Jos{\'e} Varela",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.40/",
pages = "634--648",
ISBN = "9789403901411",
abstract = "This study examines errors in a ChatGPT-mediated translation of a German economic text on inflation into Spanish, post-edited by 20 translation students. The analysis classifies 132 annotated instances by error origin (ChatGPT-generated versus student-introduced during post-editing) and by lin-guistic category. Results show that termi-nology is the highest-risk domain across the entire workflow (34.1{\%}), followed by tense/aspect (15.2{\%}) and style (13.6{\%}). ChatGPT-related errors account for 50.8{\%} of all instances, while student-introduced errors through over-editing represent 21.2{\%}. A further 28.0{\%} reflect acceptable alterna-tive reformulations. Students tend to trust fluent machine output even when it con-tains subtle semantic distortions, yet they also over-edit segments that are already ac-ceptable. The findings highlight three di-dactic priorities: developing LLM-based MT literacy, strengthening decision-making strategies in post-editing, and fostering gen-re- and domain-sensitive editing compe-tence. Implications for translator training and structured post-editing protocols are discussed."
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<namePart type="given">Ayla</namePart>
<namePart type="family">Rigouts Terryn</namePart>
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<abstract>This study examines errors in a ChatGPT-mediated translation of a German economic text on inflation into Spanish, post-edited by 20 translation students. The analysis classifies 132 annotated instances by error origin (ChatGPT-generated versus student-introduced during post-editing) and by lin-guistic category. Results show that termi-nology is the highest-risk domain across the entire workflow (34.1%), followed by tense/aspect (15.2%) and style (13.6%). ChatGPT-related errors account for 50.8% of all instances, while student-introduced errors through over-editing represent 21.2%. A further 28.0% reflect acceptable alterna-tive reformulations. Students tend to trust fluent machine output even when it con-tains subtle semantic distortions, yet they also over-edit segments that are already ac-ceptable. The findings highlight three di-dactic priorities: developing LLM-based MT literacy, strengthening decision-making strategies in post-editing, and fostering gen-re- and domain-sensitive editing compe-tence. Implications for translator training and structured post-editing protocols are discussed.</abstract>
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%0 Conference Proceedings
%T Meaning-Making Process and Error Dynamics in ChatGPT-Mediated Translation
%A Mihalache, Iulia
%A Salinas, María-José Varela
%Y Shterionov, Dimitar
%Y Vanmassenhove, Eva
%Y De Sisto, Mirella
%Y Blain, Fred
%Y Pourmostafa Roshan Sharami, Javad
%Y Lepp, Lisa
%Y Manna, Chiara
%Y Rescigno, Argentina Anna
%Y Karakanta, Alina
%Y Rigouts Terryn, Ayla
%Y Lardelli, Manuel
%Y Resende, Natalia
%Y Murgolo, Elena
%Y Hackenbuchner, Janiça
%Y Zaretskaya, Anna
%Y Esplà-Gomis, Miquel
%Y Etchegoyhen, Thierry
%Y Gromann, Dagmar
%Y Bawden, Rachel
%Y Haddow, Barry
%Y Szoc, Sara
%Y Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, The Netherlands
%@ 9789403901411
%F mihalache-salinas-2026-meaning
%X This study examines errors in a ChatGPT-mediated translation of a German economic text on inflation into Spanish, post-edited by 20 translation students. The analysis classifies 132 annotated instances by error origin (ChatGPT-generated versus student-introduced during post-editing) and by lin-guistic category. Results show that termi-nology is the highest-risk domain across the entire workflow (34.1%), followed by tense/aspect (15.2%) and style (13.6%). ChatGPT-related errors account for 50.8% of all instances, while student-introduced errors through over-editing represent 21.2%. A further 28.0% reflect acceptable alterna-tive reformulations. Students tend to trust fluent machine output even when it con-tains subtle semantic distortions, yet they also over-edit segments that are already ac-ceptable. The findings highlight three di-dactic priorities: developing LLM-based MT literacy, strengthening decision-making strategies in post-editing, and fostering gen-re- and domain-sensitive editing compe-tence. Implications for translator training and structured post-editing protocols are discussed.
%U https://aclanthology.org/2026.eamt-1.40/
%P 634-648
Markdown (Informal)
[Meaning-Making Process and Error Dynamics in ChatGPT-Mediated Translation](https://aclanthology.org/2026.eamt-1.40/) (Mihalache & Salinas, EAMT 2026)
ACL