@inproceedings{ivanovs-etal-2026-mitigating,
title = "Mitigating Gender Bias in {E}nglish-{U}krainian Machine Translation Models",
author = "Ivanovs, Pavels and
Welsh, Gina and
Selenica, Irini",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.14/",
pages = "173--189",
ISBN = "9789403901411",
abstract = "This study investigates the presence and mitigation of gender bias in English-Ukrainian machine translation (MT) models. We focused on the transfer of gender bias in two English-Ukrainian MT models, using sentences that contained professional occupation names. We evaluated two gender bias mitigation methods: 1) gender tagging of source sentences, and 2) gender bias correction by Lapa LLM, a Ukrainian large language model (LLM), using a dataset that we curated for our evaluation. Our results showed that both zero-shot models contained English-Ukrainian gender bias transfer, particularly for gender-stereotypical occupations. The gender tagging mitigation method demonstrated changes to the gender assignment in our data; however, these changes led to mixed results in gender bias correction. The Lapa LLM-correction method had promising results in demonstrating a considerable mitigation of bias in our evaluation set. Overall, our study contributes a framework for the evaluation of gender bias in English-Ukrainian translation that could potentially be applied to translation pairs in other languages."
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<abstract>This study investigates the presence and mitigation of gender bias in English-Ukrainian machine translation (MT) models. We focused on the transfer of gender bias in two English-Ukrainian MT models, using sentences that contained professional occupation names. We evaluated two gender bias mitigation methods: 1) gender tagging of source sentences, and 2) gender bias correction by Lapa LLM, a Ukrainian large language model (LLM), using a dataset that we curated for our evaluation. Our results showed that both zero-shot models contained English-Ukrainian gender bias transfer, particularly for gender-stereotypical occupations. The gender tagging mitigation method demonstrated changes to the gender assignment in our data; however, these changes led to mixed results in gender bias correction. The Lapa LLM-correction method had promising results in demonstrating a considerable mitigation of bias in our evaluation set. Overall, our study contributes a framework for the evaluation of gender bias in English-Ukrainian translation that could potentially be applied to translation pairs in other languages.</abstract>
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%0 Conference Proceedings
%T Mitigating Gender Bias in English-Ukrainian Machine Translation Models
%A Ivanovs, Pavels
%A Welsh, Gina
%A Selenica, Irini
%Y Shterionov, Dimitar
%Y Vanmassenhove, Eva
%Y De Sisto, Mirella
%Y Blain, Fred
%Y Pourmostafa Roshan Sharami, Javad
%Y Lepp, Lisa
%Y Manna, Chiara
%Y Rescigno, Argentina Anna
%Y Karakanta, Alina
%Y Rigouts Terryn, Ayla
%Y Lardelli, Manuel
%Y Resende, Natalia
%Y Murgolo, Elena
%Y Hackenbuchner, Janiça
%Y Zaretskaya, Anna
%Y Esplà-Gomis, Miquel
%Y Etchegoyhen, Thierry
%Y Gromann, Dagmar
%Y Bawden, Rachel
%Y Haddow, Barry
%Y Szoc, Sara
%Y Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, The Netherlands
%@ 9789403901411
%F ivanovs-etal-2026-mitigating
%X This study investigates the presence and mitigation of gender bias in English-Ukrainian machine translation (MT) models. We focused on the transfer of gender bias in two English-Ukrainian MT models, using sentences that contained professional occupation names. We evaluated two gender bias mitigation methods: 1) gender tagging of source sentences, and 2) gender bias correction by Lapa LLM, a Ukrainian large language model (LLM), using a dataset that we curated for our evaluation. Our results showed that both zero-shot models contained English-Ukrainian gender bias transfer, particularly for gender-stereotypical occupations. The gender tagging mitigation method demonstrated changes to the gender assignment in our data; however, these changes led to mixed results in gender bias correction. The Lapa LLM-correction method had promising results in demonstrating a considerable mitigation of bias in our evaluation set. Overall, our study contributes a framework for the evaluation of gender bias in English-Ukrainian translation that could potentially be applied to translation pairs in other languages.
%U https://aclanthology.org/2026.eamt-1.14/
%P 173-189
Markdown (Informal)
[Mitigating Gender Bias in English-Ukrainian Machine Translation Models](https://aclanthology.org/2026.eamt-1.14/) (Ivanovs et al., EAMT 2026)
ACL