Reasoning About Gender: How Source Text Strategies Impact Italian–to-German Machine Translation Beyond the Binary

Reasoning LLMs outperform standard inference at shifting Italian-to-German machine translation from binary to non-binary formulations, according to a paper by Paolo Di Natale, Laura Schlutter, Elena Chiocchetti, and Marlies Alber published in the Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies (GITT 2026), pages 49–72. The authors used a controlled test set with binary and non-binary source strategies plus an automatic evaluation framework classifying target sentences as non-binary, binary-gendered, single-gendered, or incoherent, and found reasoning models handled epicene terms and special characters better but showed no improvement in sentence-level consistency or evaluation accuracy. Qualitative analysis of the German translations showed reasoning encouraged neutralization, visibility strategies, and paraphrasing, producing more natural target texts.

Abstract This paper investigates how gender-fair strategies in source texts influence the production of non-binary translations in the Italian to German combination. We make use of a controlled test set featuring both binary and non-binary approaches to assess their effectiveness for non-binary renderings in the target language. We also introduce an automatic evaluation framework that classifies target sentences into four categories: non-binary, binary-gendered, single-gendered, and incoherent. Relying on human annotation and analysis, we compare Reasoning LLMs against standard inference, examining whether reasoning improves translation quality and automatic evaluation. Our results show that reasoning models are more successful in shifting from binary to non-binary formulations and in handling linguistic challenges such as epicene terms and special characters, although there are no improvements in sentence-level consistency and evaluation accuracy. A qualitative analysis of German translations shows that reasoning encourages the reformulation of source-side strategies through neutralization, visibility strategies, and paraphrasing, resulting in more natural target texts. - Anthology ID: - 2026.gitt-1.5 - Volume: - Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies GITT 2026 https://aclanthology.org/volumes/2026.gitt-1/ - Month: - June - Year: - 2026 - Address: - Tilburg, the Netherlands - Editors: - Manuel Lardelli https://aclanthology.org/people/manuel-lardelli/ , Beatrice Savoldi https://aclanthology.org/people/beatrice-savoldi/ , Janiça Hackenbuchner https://aclanthology.org/people/janica-hackenbuchner/ , Luisa Bentivogli https://aclanthology.org/people/luisa-bentivogli/ , Eleni Gkovedarou https://aclanthology.org/people/eleni-gkovedarou/ , Joke Daems https://aclanthology.org/people/joke-daems/ - Venues: - GITT https://aclanthology.org/venues/gitt/ | WS https://aclanthology.org/venues/ws/ - SIG: - Publisher: - European Association for Machine Translation - Note: - Pages: - 49–72 - Language: - URL: - https://aclanthology.org/2026.gitt-1.5/ https://aclanthology.org/2026.gitt-1.5/ - DOI: - Cite ACL : - Paolo Di Natale, Laura Schlutter, Elena Chiocchetti, and Marlies Alber. 2026. Reasoning About Gender: How Source Text Strategies Impact Italian–to-German Machine Translation Beyond the Binary https://aclanthology.org/2026.gitt-1.5/ . In Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies GITT 2026 , pages 49–72, Tilburg, the Netherlands. European Association for Machine Translation. - Cite Informal : - Reasoning About Gender: How Source Text Strategies Impact Italian–to-German Machine Translation Beyond the Binary https://aclanthology.org/2026.gitt-1.5/ Di Natale et al., GITT 2026 - PDF: - https://aclanthology.org/2026.gitt-1.5.pdf https://aclanthology.org/2026.gitt-1.5.pdf