@inproceedings{manakhimova-lapshinova-koltunski-2026-emotions,
title = "Can Emotions Signal Gender? Investigating Implicit Cues in Human and {LLM} Translations of {A}mazon Reviews",
author = "Manakhimova, Shushen and
Lapshinova-Koltunski, Ekaterina",
editor = "Lardelli, Manuel and
Savoldi, Beatrice and
Hackenbuchner, Jani{\c{c}}a and
Bentivogli, Luisa and
Gkovedarou, Eleni and
Daems, Joke",
booktitle = "Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies ({GITT} 2026)",
month = jun,
year = "2026",
address = "Tilburg, the Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.gitt-1.6/",
pages = "73--80",
abstract = "This study investigates whether source-text emotion is associated with grammatical gender choices in English-Russian translation when the author{'}s gender is not specified. Building on Popovi{\'c} and Lapshinova-Koltunski (2024), we analyse first-person grammatical gender in Amazon review translations produced by professional translators, translation students, and three LLMs: GPT-4, Llama, and Mistral. We assign emotion labels to the English source reviews and test whether these labels are associated with masculine or feminine forms in the Russian translations. Reviews labelled as expressing love are more likely to be translated with feminine grammatical gender by both human translator groups, with small-to-moderate effect sizes. This pattern is absent in GPT-4 and Mistral, but appears in Llama. Other frequent emotion labels do not show the same positive association. We therefore treat the findings as exploratory evidence that specific source-text emotions may be associated with gendered translation choices, while emphasizing the need for larger-scale validation across additional emotions, genres, and language pairs."
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<abstract>This study investigates whether source-text emotion is associated with grammatical gender choices in English-Russian translation when the author’s gender is not specified. Building on Popović and Lapshinova-Koltunski (2024), we analyse first-person grammatical gender in Amazon review translations produced by professional translators, translation students, and three LLMs: GPT-4, Llama, and Mistral. We assign emotion labels to the English source reviews and test whether these labels are associated with masculine or feminine forms in the Russian translations. Reviews labelled as expressing love are more likely to be translated with feminine grammatical gender by both human translator groups, with small-to-moderate effect sizes. This pattern is absent in GPT-4 and Mistral, but appears in Llama. Other frequent emotion labels do not show the same positive association. We therefore treat the findings as exploratory evidence that specific source-text emotions may be associated with gendered translation choices, while emphasizing the need for larger-scale validation across additional emotions, genres, and language pairs.</abstract>
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%0 Conference Proceedings
%T Can Emotions Signal Gender? Investigating Implicit Cues in Human and LLM Translations of Amazon Reviews
%A Manakhimova, Shushen
%A Lapshinova-Koltunski, Ekaterina
%Y Lardelli, Manuel
%Y Savoldi, Beatrice
%Y Hackenbuchner, Janiça
%Y Bentivogli, Luisa
%Y Gkovedarou, Eleni
%Y Daems, Joke
%S Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies (GITT 2026)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, the Netherlands
%F manakhimova-lapshinova-koltunski-2026-emotions
%X This study investigates whether source-text emotion is associated with grammatical gender choices in English-Russian translation when the author’s gender is not specified. Building on Popović and Lapshinova-Koltunski (2024), we analyse first-person grammatical gender in Amazon review translations produced by professional translators, translation students, and three LLMs: GPT-4, Llama, and Mistral. We assign emotion labels to the English source reviews and test whether these labels are associated with masculine or feminine forms in the Russian translations. Reviews labelled as expressing love are more likely to be translated with feminine grammatical gender by both human translator groups, with small-to-moderate effect sizes. This pattern is absent in GPT-4 and Mistral, but appears in Llama. Other frequent emotion labels do not show the same positive association. We therefore treat the findings as exploratory evidence that specific source-text emotions may be associated with gendered translation choices, while emphasizing the need for larger-scale validation across additional emotions, genres, and language pairs.
%U https://aclanthology.org/2026.gitt-1.6/
%P 73-80
Markdown (Informal)
[Can Emotions Signal Gender? Investigating Implicit Cues in Human and LLM Translations of Amazon Reviews](https://aclanthology.org/2026.gitt-1.6/) (Manakhimova & Lapshinova-Koltunski, GITT 2026)
ACL