@inproceedings{hackenbuchner-etal-2025-genderous,
title = "{GENDEROUS}: Machine Translation and Cross-Linguistic Evaluation of a Gender-Ambiguous Dataset",
author = "Hackenbuchner, Jani{\c{c}}a and
Gkovedarou, Eleni and
Daems, Joke",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Sta{\'n}czak, Karolina and
Nozza, Debora",
booktitle = "Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.gebnlp-1.27/",
doi = "10.18653/v1/2025.gebnlp-1.27",
pages = "302--319",
ISBN = "979-8-89176-277-0",
abstract = "Contributing to research on gender beyond the binary, this work introduces GENDEROUS, a dataset of gender-ambiguous sentences containing gender-marked occupations and adjectives, and sentences with the ambiguous or non-binary pronoun their. We cross-linguistically evaluate how machine translation (MT) systems and large language models (LLMs) translate these sentences from English into four grammatical gender languages: Greek, German, Spanish and Dutch. We show the systems' continued default to male-gendered translations, with exceptions (particularly for Dutch). Prompting for alternatives, however, shows potential in attaining more diverse and neutral translations across all languages. An LLM-as-a-judge approach was implemented, where benchmarking against gold standards emphasises the continued need for human annotations."
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<abstract>Contributing to research on gender beyond the binary, this work introduces GENDEROUS, a dataset of gender-ambiguous sentences containing gender-marked occupations and adjectives, and sentences with the ambiguous or non-binary pronoun their. We cross-linguistically evaluate how machine translation (MT) systems and large language models (LLMs) translate these sentences from English into four grammatical gender languages: Greek, German, Spanish and Dutch. We show the systems’ continued default to male-gendered translations, with exceptions (particularly for Dutch). Prompting for alternatives, however, shows potential in attaining more diverse and neutral translations across all languages. An LLM-as-a-judge approach was implemented, where benchmarking against gold standards emphasises the continued need for human annotations.</abstract>
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%0 Conference Proceedings
%T GENDEROUS: Machine Translation and Cross-Linguistic Evaluation of a Gender-Ambiguous Dataset
%A Hackenbuchner, Janiça
%A Gkovedarou, Eleni
%A Daems, Joke
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Stańczak, Karolina
%Y Nozza, Debora
%S Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-277-0
%F hackenbuchner-etal-2025-genderous
%X Contributing to research on gender beyond the binary, this work introduces GENDEROUS, a dataset of gender-ambiguous sentences containing gender-marked occupations and adjectives, and sentences with the ambiguous or non-binary pronoun their. We cross-linguistically evaluate how machine translation (MT) systems and large language models (LLMs) translate these sentences from English into four grammatical gender languages: Greek, German, Spanish and Dutch. We show the systems’ continued default to male-gendered translations, with exceptions (particularly for Dutch). Prompting for alternatives, however, shows potential in attaining more diverse and neutral translations across all languages. An LLM-as-a-judge approach was implemented, where benchmarking against gold standards emphasises the continued need for human annotations.
%R 10.18653/v1/2025.gebnlp-1.27
%U https://aclanthology.org/2025.gebnlp-1.27/
%U https://doi.org/10.18653/v1/2025.gebnlp-1.27
%P 302-319
Markdown (Informal)
[GENDEROUS: Machine Translation and Cross-Linguistic Evaluation of a Gender-Ambiguous Dataset](https://aclanthology.org/2025.gebnlp-1.27/) (Hackenbuchner et al., GeBNLP 2025)
ACL