@inproceedings{garg-etal-2024-generating,
title = "Generating Gender Alternatives in Machine Translation",
author = "Garg, Sarthak and
Gheini, Mozhdeh and
Emmanuel, Clara and
Likhomanenko, Tatiana and
Gao, Qin and
Paulik, Matthias",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.15",
pages = "237--254",
abstract = "Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term {``}the nurse{''}) into the gendered form that is most prevalent in the systems{'} training data (e.g., {``}enfermera{''}, the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead.",
}
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<abstract>Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term “the nurse”) into the gendered form that is most prevalent in the systems’ training data (e.g., “enfermera”, the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead.</abstract>
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%0 Conference Proceedings
%T Generating Gender Alternatives in Machine Translation
%A Garg, Sarthak
%A Gheini, Mozhdeh
%A Emmanuel, Clara
%A Likhomanenko, Tatiana
%A Gao, Qin
%A Paulik, Matthias
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Goldfarb-Tarrant, Seraphina
%Y Nozza, Debora
%S Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F garg-etal-2024-generating
%X Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term “the nurse”) into the gendered form that is most prevalent in the systems’ training data (e.g., “enfermera”, the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead.
%U https://aclanthology.org/2024.gebnlp-1.15
%P 237-254
Markdown (Informal)
[Generating Gender Alternatives in Machine Translation](https://aclanthology.org/2024.gebnlp-1.15) (Garg et al., GeBNLP-WS 2024)
ACL
- Sarthak Garg, Mozhdeh Gheini, Clara Emmanuel, Tatiana Likhomanenko, Qin Gao, and Matthias Paulik. 2024. Generating Gender Alternatives in Machine Translation. In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 237–254, Bangkok, Thailand. Association for Computational Linguistics.