@inproceedings{scourneau-pires-2026-blainded,
title = "{B}l{AI}nded by Fluency: How Idiomatic Machine Translation Outputs Affect Student Post-Editors' Edit Types",
author = {Scourneau, Valentin and
Pires, Lo{\"i}c De Faria},
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-1.56/",
pages = "869--882",
ISBN = "9789403901411",
abstract = "This study explores the influence of two prompting strategies on the lexical and syntactic metrics of the LLM-powered machine translations (MTs) of a corpus of 18{\textasciitilde}British editorials into French as well as their impact on the edit types made by Master{'}s translation students post-editing from a representative editorial of the corpus, as evaluated using the MTPEAS taxonomy. Quantitatively, the prompt specifically requesting more syntactic and lexical variety leads to significantly higher syntactic and lexical metrics scores in the MTs, but differences remain significant only for lexical metrics in the post-edited versions of the representative editorial. Qualitatively, we show that students post-editing from an MT featuring more idiomatic rephrasings and fewer syntactic calques (as opposed to an MT that is structurally closer to the source text) seem to make fewer edits overall, leave more MT errors unaddressed, and make fewer successful edits."
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<namePart type="given">Ayla</namePart>
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<abstract>This study explores the influence of two prompting strategies on the lexical and syntactic metrics of the LLM-powered machine translations (MTs) of a corpus of 18~British editorials into French as well as their impact on the edit types made by Master’s translation students post-editing from a representative editorial of the corpus, as evaluated using the MTPEAS taxonomy. Quantitatively, the prompt specifically requesting more syntactic and lexical variety leads to significantly higher syntactic and lexical metrics scores in the MTs, but differences remain significant only for lexical metrics in the post-edited versions of the representative editorial. Qualitatively, we show that students post-editing from an MT featuring more idiomatic rephrasings and fewer syntactic calques (as opposed to an MT that is structurally closer to the source text) seem to make fewer edits overall, leave more MT errors unaddressed, and make fewer successful edits.</abstract>
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%0 Conference Proceedings
%T BlAInded by Fluency: How Idiomatic Machine Translation Outputs Affect Student Post-Editors’ Edit Types
%A Scourneau, Valentin
%A Pires, Loïc De Faria
%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 scourneau-pires-2026-blainded
%X This study explores the influence of two prompting strategies on the lexical and syntactic metrics of the LLM-powered machine translations (MTs) of a corpus of 18~British editorials into French as well as their impact on the edit types made by Master’s translation students post-editing from a representative editorial of the corpus, as evaluated using the MTPEAS taxonomy. Quantitatively, the prompt specifically requesting more syntactic and lexical variety leads to significantly higher syntactic and lexical metrics scores in the MTs, but differences remain significant only for lexical metrics in the post-edited versions of the representative editorial. Qualitatively, we show that students post-editing from an MT featuring more idiomatic rephrasings and fewer syntactic calques (as opposed to an MT that is structurally closer to the source text) seem to make fewer edits overall, leave more MT errors unaddressed, and make fewer successful edits.
%U https://aclanthology.org/2026.eamt-1.56/
%P 869-882
Markdown (Informal)
[BlAInded by Fluency: How Idiomatic Machine Translation Outputs Affect Student Post-Editors’ Edit Types](https://aclanthology.org/2026.eamt-1.56/) (Scourneau & Pires, EAMT 2026)
ACL