@inproceedings{manakhimova-etal-2026-love,
title = "Love, Anger, Sadness, or Just a Rating? Gender Assignment in Human and {LLM} Translations of {A}mazon Reviews",
author = "Manakhimova, Shushen and
Lapshinova-Koltunski, Ekaterina and
Pitroda, Khushi and
Popovi{\'c}, Maja",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Sta{\'n}czak, Karolina and
Nozza, Debora and
Attanasio, Giuseppe and
Neplenbroek, Vera and
Hardmeier, Christian",
booktitle = "Proceedings of the 7th Workshop on Gender Bias in Natural Language Processing ({G}e{BNLP})",
month = nov,
year = "2026",
address = "Hengqin, China (remote)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.gebnlp-1.10/",
pages = "145--156",
ISBN = "979-8-89176-447-7",
abstract = "We investigate whether product ratings and source-text emotions are associated with first-person gender choices in English-to-Russian and English-to-Croatian translations by professional translators, students, and three LLMs (GPT-4o mini, Llama, and Mistral). We score love, sadness, and anger in the source reviews with a RoBERTa classifier fine-tuned on GoEmotions and fit logistic mixed-effects models of feminine versus masculine forms, accounting for product category, translator group, and repeated translations of the same review. Reviews with high ratings and reviews written in more positive language receive feminine forms more often in both languages. All emotion estimates point in the directions gender stereotypes predict, love and sadness toward feminine forms and anger toward masculine ones, but only love in Russian is statistically significant. We find evidence that emotion-gender associations vary across translator groups in Russian, but no clear evidence of such variation in Croatian."
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<abstract>We investigate whether product ratings and source-text emotions are associated with first-person gender choices in English-to-Russian and English-to-Croatian translations by professional translators, students, and three LLMs (GPT-4o mini, Llama, and Mistral). We score love, sadness, and anger in the source reviews with a RoBERTa classifier fine-tuned on GoEmotions and fit logistic mixed-effects models of feminine versus masculine forms, accounting for product category, translator group, and repeated translations of the same review. Reviews with high ratings and reviews written in more positive language receive feminine forms more often in both languages. All emotion estimates point in the directions gender stereotypes predict, love and sadness toward feminine forms and anger toward masculine ones, but only love in Russian is statistically significant. We find evidence that emotion-gender associations vary across translator groups in Russian, but no clear evidence of such variation in Croatian.</abstract>
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%0 Conference Proceedings
%T Love, Anger, Sadness, or Just a Rating? Gender Assignment in Human and LLM Translations of Amazon Reviews
%A Manakhimova, Shushen
%A Lapshinova-Koltunski, Ekaterina
%A Pitroda, Khushi
%A Popović, Maja
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Stańczak, Karolina
%Y Nozza, Debora
%Y Attanasio, Giuseppe
%Y Neplenbroek, Vera
%Y Hardmeier, Christian
%S Proceedings of the 7th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China (remote)
%@ 979-8-89176-447-7
%F manakhimova-etal-2026-love
%X We investigate whether product ratings and source-text emotions are associated with first-person gender choices in English-to-Russian and English-to-Croatian translations by professional translators, students, and three LLMs (GPT-4o mini, Llama, and Mistral). We score love, sadness, and anger in the source reviews with a RoBERTa classifier fine-tuned on GoEmotions and fit logistic mixed-effects models of feminine versus masculine forms, accounting for product category, translator group, and repeated translations of the same review. Reviews with high ratings and reviews written in more positive language receive feminine forms more often in both languages. All emotion estimates point in the directions gender stereotypes predict, love and sadness toward feminine forms and anger toward masculine ones, but only love in Russian is statistically significant. We find evidence that emotion-gender associations vary across translator groups in Russian, but no clear evidence of such variation in Croatian.
%U https://aclanthology.org/2026.gebnlp-1.10/
%P 145-156
Markdown (Informal)
[Love, Anger, Sadness, or Just a Rating? Gender Assignment in Human and LLM Translations of Amazon Reviews](https://aclanthology.org/2026.gebnlp-1.10/) (Manakhimova et al., GeBNLP 2026)
ACL