@inproceedings{lardelli-etal-2024-gefmt,
title = "{G}e{FMT}: Gender-Fair Language in {G}erman Machine Translation",
author = "Lardelli, Manuel and
Lauscher, Anne and
Attanasio, Giuseppe",
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
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)",
month = jun,
year = "2024",
address = "Sheffield, UK",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.eamt-2.19",
pages = "37--38",
abstract = "Research on gender bias in Machine Translation (MT) predominantly focuses on binary gender or few languages. In this project, we investigate the ability of commercial MT systems and neural models to translate using gender-fair language (GFL) from English into German. We enrich a community-created GFL dictionary, and sample multi-sentence test instances from encyclopedic text and parliamentary speeches. We translate our resources with different MT systems and open-weights models. We also plan to post-edit biased outputs with professionals and share them publicly. The outcome will constitute a new resource for automatic evaluation and modeling gender-fair EN-DE MT.",
}
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<abstract>Research on gender bias in Machine Translation (MT) predominantly focuses on binary gender or few languages. In this project, we investigate the ability of commercial MT systems and neural models to translate using gender-fair language (GFL) from English into German. We enrich a community-created GFL dictionary, and sample multi-sentence test instances from encyclopedic text and parliamentary speeches. We translate our resources with different MT systems and open-weights models. We also plan to post-edit biased outputs with professionals and share them publicly. The outcome will constitute a new resource for automatic evaluation and modeling gender-fair EN-DE MT.</abstract>
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%0 Conference Proceedings
%T GeFMT: Gender-Fair Language in German Machine Translation
%A Lardelli, Manuel
%A Lauscher, Anne
%A Attanasio, Giuseppe
%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 Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F lardelli-etal-2024-gefmt
%X Research on gender bias in Machine Translation (MT) predominantly focuses on binary gender or few languages. In this project, we investigate the ability of commercial MT systems and neural models to translate using gender-fair language (GFL) from English into German. We enrich a community-created GFL dictionary, and sample multi-sentence test instances from encyclopedic text and parliamentary speeches. We translate our resources with different MT systems and open-weights models. We also plan to post-edit biased outputs with professionals and share them publicly. The outcome will constitute a new resource for automatic evaluation and modeling gender-fair EN-DE MT.
%U https://aclanthology.org/2024.eamt-2.19
%P 37-38
Markdown (Informal)
[GeFMT: Gender-Fair Language in German Machine Translation](https://aclanthology.org/2024.eamt-2.19) (Lardelli et al., EAMT 2024)
ACL
- Manuel Lardelli, Anne Lauscher, and Giuseppe Attanasio. 2024. GeFMT: Gender-Fair Language in German Machine Translation. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2), pages 37–38, Sheffield, UK. European Association for Machine Translation (EAMT).