@inproceedings{alsulami-2026-potential,
title = "The Potential of Large Language Models for Translating Tourism Promotional Texts: A Mixed-methods Study",
author = "Alsulami, Raghad",
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-1.39/",
pages = "620--633",
ISBN = "9789403901411",
abstract = "This paper reports on a mixed-methods pilot study examining the potential of a large language model (LLM) for translating tourism promotional texts (TPTs), in comparison with a conventional neural machine translation (NMT) system, from English into Arabic. Four professional translators participated in a post-editing experiment followed by semi-structured interviews involving cue-based retrospection. The post-editing task aimed to provide empirical evidence of the effort involved in working with TPTs, while the interviews sought to capture participants' judgments and subjective evaluations of the outputs. Overall, most participants exerted less effort post-editing LLM-generated outputs to a publishable standard compared to NMT outputs. They perceived the LLM outputs to be more creative, with creativity manifested through non-literal translations and aesthetic augmentation, while also noting that the outputs were unpredictable and far from perfect; in contrast, the NMT outputs were generally viewed as more informative yet lacking the promotional appeal needed for TPTs. The paper concludes by outlining the implications of the findings and suggesting several avenues for future research."
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<abstract>This paper reports on a mixed-methods pilot study examining the potential of a large language model (LLM) for translating tourism promotional texts (TPTs), in comparison with a conventional neural machine translation (NMT) system, from English into Arabic. Four professional translators participated in a post-editing experiment followed by semi-structured interviews involving cue-based retrospection. The post-editing task aimed to provide empirical evidence of the effort involved in working with TPTs, while the interviews sought to capture participants’ judgments and subjective evaluations of the outputs. Overall, most participants exerted less effort post-editing LLM-generated outputs to a publishable standard compared to NMT outputs. They perceived the LLM outputs to be more creative, with creativity manifested through non-literal translations and aesthetic augmentation, while also noting that the outputs were unpredictable and far from perfect; in contrast, the NMT outputs were generally viewed as more informative yet lacking the promotional appeal needed for TPTs. The paper concludes by outlining the implications of the findings and suggesting several avenues for future research.</abstract>
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%0 Conference Proceedings
%T The Potential of Large Language Models for Translating Tourism Promotional Texts: A Mixed-methods Study
%A Alsulami, Raghad
%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 alsulami-2026-potential
%X This paper reports on a mixed-methods pilot study examining the potential of a large language model (LLM) for translating tourism promotional texts (TPTs), in comparison with a conventional neural machine translation (NMT) system, from English into Arabic. Four professional translators participated in a post-editing experiment followed by semi-structured interviews involving cue-based retrospection. The post-editing task aimed to provide empirical evidence of the effort involved in working with TPTs, while the interviews sought to capture participants’ judgments and subjective evaluations of the outputs. Overall, most participants exerted less effort post-editing LLM-generated outputs to a publishable standard compared to NMT outputs. They perceived the LLM outputs to be more creative, with creativity manifested through non-literal translations and aesthetic augmentation, while also noting that the outputs were unpredictable and far from perfect; in contrast, the NMT outputs were generally viewed as more informative yet lacking the promotional appeal needed for TPTs. The paper concludes by outlining the implications of the findings and suggesting several avenues for future research.
%U https://aclanthology.org/2026.eamt-1.39/
%P 620-633
Markdown (Informal)
[The Potential of Large Language Models for Translating Tourism Promotional Texts: A Mixed-methods Study](https://aclanthology.org/2026.eamt-1.39/) (Alsulami, EAMT 2026)
ACL