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        <title>Gender Swapping as a Data Augmentation Technique: Developing Gender-Balanced Datasets for Ukrainian Language Processing</title>
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    <name type="personal">
        <namePart type="given">Olha</namePart>
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            <title>Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)</title>
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            <namePart type="given">Mariana</namePart>
            <namePart type="family">Romanyshyn</namePart>
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    <abstract>This paper presents a pipeline for generating gender-balanced datasets through sentence-level gender swapping, addressing the gender-imbalance issue in Ukrainian texts. We select sentences with gender-marked entities, focusing on job titles, generate their inverted alternatives using LLMs and human-in-the-loop, and fine-tune Aya-101 on the resulting dataset for the task of gender swapping. Additionally, we train a Named Entity Recognition (NER) model on gender-balanced data, demonstrating its ability to better recognize gendered entities. The findings unveil the potential of gender-balanced datasets to enhance model robustness and support more fair language processing. Finally, we make a gender-swapped version of NER-UK~2.0 and the fine-tuned Aya-101 model available for download and further research.</abstract>
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    <identifier type="doi">10.18653/v1/2025.unlp-1.16</identifier>
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%0 Conference Proceedings
%T Gender Swapping as a Data Augmentation Technique: Developing Gender-Balanced Datasets for Ukrainian Language Processing
%A Nahurna, Olha
%A Romanyshyn, Mariana
%Y Romanyshyn, Mariana
%S Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria (online)
%@ 979-8-89176-269-5
%F nahurna-romanyshyn-2025-gender
%X This paper presents a pipeline for generating gender-balanced datasets through sentence-level gender swapping, addressing the gender-imbalance issue in Ukrainian texts. We select sentences with gender-marked entities, focusing on job titles, generate their inverted alternatives using LLMs and human-in-the-loop, and fine-tune Aya-101 on the resulting dataset for the task of gender swapping. Additionally, we train a Named Entity Recognition (NER) model on gender-balanced data, demonstrating its ability to better recognize gendered entities. The findings unveil the potential of gender-balanced datasets to enhance model robustness and support more fair language processing. Finally, we make a gender-swapped version of NER-UK~2.0 and the fine-tuned Aya-101 model available for download and further research.
%R 10.18653/v1/2025.unlp-1.16
%U https://aclanthology.org/2025.unlp-1.16/
%U https://doi.org/10.18653/v1/2025.unlp-1.16
%P 147-161

Markdown (Informal)

[Gender Swapping as a Data Augmentation Technique: Developing Gender-Balanced Datasets for Ukrainian Language Processing](https://aclanthology.org/2025.unlp-1.16/) (Nahurna & Romanyshyn, UNLP 2025)

• Gender Swapping as a Data Augmentation Technique: Developing Gender-Balanced Datasets for Ukrainian Language Processing (Nahurna & Romanyshyn, UNLP 2025)

ACL

• Olha Nahurna and Mariana Romanyshyn. 2025. Gender Swapping as a Data Augmentation Technique: Developing Gender-Balanced Datasets for Ukrainian Language Processing. In Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025), pages 147–161, Vienna, Austria (online). Association for Computational Linguistics.