Gender Swapping as a Data Augmentation Technique: Developing Gender-Balanced Datasets for U krainian Language Processing (2025)

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.

## introduction the ukrainian language has historically exhibited a lack of gender balance in professional titles, with masculine forms traditionally dominating. to address this imbalance, the 2019 revision of ukrainian orthography 1 introduced official guidelines on the word formation of feminitives -feminine forms of personal nouns. although these changes aim to promote more inclusive and gender-balanced language, their implementation remains relatively recent and, at times, controversial @xcite . a study of trends in the usage of feminine personal nouns @xcite reveals that prior to 2019, their presence in ukrainian corpora was minimal. many texts used masculine forms even in contexts where grammatical gender agreement required a feminine equivalent. for example, in the sentence "менi допомогла оксана миколаївна, вона найкраща лiкар у мiстi." the low representation of feminitives in existing corpora has resulted in limited availability of training data containing feminine personal noun forms. at the same time, there is a growing demand for nlp models that can accurately recognize, interpret, and generate gender-marked language in ukrainian. to address this challenge, we propose a gender swapping pipeline designed to facilitate the creation of gender-balanced datasets through data augmentation. the rest of the paper is organized as follows. in section 2, we review existing research on gender bias in nlp and methods for achieving gender balance in data. section 3 presents the genderswapping pipeline for generating gender-parallel sentences. section 4 covers two experiments: the first one focuses on fine-tuning a large language model (llm) using a gender-parallel dataset, and the second one investigates whether training a named entity recognition (ner) model on genderbalanced data can improve the recognition of gendered entities. the paper ends with conclusions, limitations, and ethical considerations. ## related work this section reviews the existing research on gender bias in nlp and gives an overview of solutions for achieving gender balance in text corpora. ## proposed solution since ukrainian is a morphologically rich language with the category of grammatical gender, we focus on using cda with llms to create genderbalanced datasets. to the best of our knowledge, no prior research has explored this approach for the ukrainian language. this section describes the key components of the proposed gender swapping pipeline applicable to morphologically rich languages (see figure 1 for visualization). ## experiments this section provides a deep dive into the experimental part of our research. first, in section 4.1, we apply the proposed gender-swapping pipeline to build a gender-swapped dataset and a genderswapping model for the ukrainian language. we specifically focus on applying this data augmentation technique to the ukrainian language job titles, considering the recent shift in the use of feminitives in ukrainian and their low representation in ukrainian language corpora. then, in section 4.2, we use the developed dataset to train a ner model for ukrainian. ## conclusions in this paper, we introduced a sentence-level gender-swapping pipeline that utilizes gendermarked data. using this approach, we fine-tuned the aya-101 model on a ukrainian gender-parallel corpus, achieving substantial performance gains over the original aya-101 and performance parity with gpt-4o-mini. furthermore, we trained a ner model on an augmented gender-balanced dataset, which led to improved recognition of feminine job entities. however, performance declined on the original set, which predominantly contains masculine entities. these results highlight the potential of genderbalanced data to improve ner performance for underrepresented gender categories, while also revealing the difficulty of preserving consistent accuracy across differing gender distributions. as part of this research, we have made several key contributions available to the community: (1) a dataset of parallel gender-swapped sentences, (2) a gender-swapped ner-uk 2.0 subset of sentences with job titles, and (3) a fine-tuned aya-101 model capable of gender swapping sentences in the ukrainian language.