@inproceedings{bawden-etal-2024-translate,
title = "Translate your Own: a Post-Editing Experiment in the {NLP} domain",
author = {Bawden, Rachel and
Peng, Ziqian and
B{\'e}nard, Maud and
Clergerie, {\'E}ric and
Esamotunu, Rapha{\"e}l and
Huguin, Mathilde and
K{\"u}bler, Natalie and
Mestivier, Alexandra and
Michelot, Mona and
Romary, Laurent and
Zhu, Lichao and
Yvon, Fran{\c{c}}ois},
editor = "Scarton, Carolina and
Prescott, Charlotte and
Bayliss, Chris and
Oakley, Chris and
Wright, Joanna and
Wrigley, Stuart and
Song, Xingyi and
Gow-Smith, Edward and
Bawden, Rachel and
S{\'a}nchez-Cartagena, V{\'\i}ctor M and
Cadwell, Patrick and
Lapshinova-Koltunski, Ekaterina and
Cabarr{\~a}o, Vera and
Chatzitheodorou, Konstantinos and
Nurminen, Mary and
Kanojia, Diptesh and
Moniz, Helena",
booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)",
month = jun,
year = "2024",
address = "Sheffield, UK",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.eamt-1.36",
pages = "431--443",
abstract = "The improvements in neural machine translation make translation and post-editing pipelines ever more effective for a wider range of applications. In this paper, we evaluate the effectiveness of such a pipeline for the translation of scientific documents (limited here to article abstracts). Using a dedicated interface, we collect, then analyse the post-edits of approximately 350 abstracts (English→French) in the Natural Language Processing domain for two groups of post-editors: domain experts (academics encouraged to post-edit their own articles) on the one hand and trained translators on the other. Our results confirm that such pipelines can be effective, at least for high-resource language pairs. They also highlight the difference in the post-editing strategy of the two subgroups. Finally, they suggest that working on term translation is the most pressing issue to improve fully automatic translations, but that in a post-editing setup, other error types can be equally annoying for post-editors.",
}
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%0 Conference Proceedings
%T Translate your Own: a Post-Editing Experiment in the NLP domain
%A Bawden, Rachel
%A Peng, Ziqian
%A Bénard, Maud
%A Clergerie, Éric
%A Esamotunu, Raphaël
%A Huguin, Mathilde
%A Kübler, Natalie
%A Mestivier, Alexandra
%A Michelot, Mona
%A Romary, Laurent
%A Zhu, Lichao
%A Yvon, François
%Y Scarton, Carolina
%Y Prescott, Charlotte
%Y Bayliss, Chris
%Y Oakley, Chris
%Y Wright, Joanna
%Y Wrigley, Stuart
%Y Song, Xingyi
%Y Gow-Smith, Edward
%Y Bawden, Rachel
%Y Sánchez-Cartagena, Víctor M.
%Y Cadwell, Patrick
%Y Lapshinova-Koltunski, Ekaterina
%Y Cabarrão, Vera
%Y Chatzitheodorou, Konstantinos
%Y Nurminen, Mary
%Y Kanojia, Diptesh
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F bawden-etal-2024-translate
%X The improvements in neural machine translation make translation and post-editing pipelines ever more effective for a wider range of applications. In this paper, we evaluate the effectiveness of such a pipeline for the translation of scientific documents (limited here to article abstracts). Using a dedicated interface, we collect, then analyse the post-edits of approximately 350 abstracts (English→French) in the Natural Language Processing domain for two groups of post-editors: domain experts (academics encouraged to post-edit their own articles) on the one hand and trained translators on the other. Our results confirm that such pipelines can be effective, at least for high-resource language pairs. They also highlight the difference in the post-editing strategy of the two subgroups. Finally, they suggest that working on term translation is the most pressing issue to improve fully automatic translations, but that in a post-editing setup, other error types can be equally annoying for post-editors.
%U https://aclanthology.org/2024.eamt-1.36
%P 431-443
Markdown (Informal)
[Translate your Own: a Post-Editing Experiment in the NLP domain](https://aclanthology.org/2024.eamt-1.36) (Bawden et al., EAMT 2024)
ACL
- Rachel Bawden, Ziqian Peng, Maud Bénard, Éric Clergerie, Raphaël Esamotunu, Mathilde Huguin, Natalie Kübler, Alexandra Mestivier, Mona Michelot, Laurent Romary, Lichao Zhu, and François Yvon. 2024. Translate your Own: a Post-Editing Experiment in the NLP domain. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1), pages 431–443, Sheffield, UK. European Association for Machine Translation (EAMT).