@inproceedings{bove-2025-gender,
    title = "Gender-inclusive language and machine translation: from {S}panish into {I}talian",
    author = "Bove, Antonella",
    editor = "Hackenbuchner, Jani{\c{c}}a  and
      Bentivogli, Luisa  and
      Daems, Joke  and
      Manna, Chiara  and
      Savoldi, Beatrice  and
      Vanmassenhove, Eva",
    booktitle = "Proceedings of the 3rd Workshop on Gender-Inclusive Translation Technologies (GITT 2025)",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.gitt-1.6/",
    pages = "89--90",
    ISBN = "978-2-9701897-4-9",
    abstract = "Gender-inclusive language is a discursive practice that introduces the use of new forms and strategies to make women and different non-binary gender identities more visible. Spanish uses gender doublets (los ni{\~n}os y las ni{\~n}as, los/as candidatos/as), the neomorpheme -e, and typographic signs such as @ and x. Similarly, Italian employs gender doublets (i bambini e le bambine, i/le candidati/e), the schwa ({\textschwa}) as a neomorpheme, and the asterisk (*) as a typographic sign. Strategies like gender doublet and the @ sign aims at making women visible from a binary perspective; the others are intended to give visibility to non-binary gender identities as well (Escandell-Vidal 2020, Giusti 2022). Without a clear and agreed standard, inclusive translation poses a significant challenge and a great social responsibility for translation professionals. Hence, it is crucial to study and evaluate the quality of the outputs generated by machine translation systems (Kornacki {\&} Pietrzak 2025, Pfalzgraf 2024). This paper contributes to the understanding of this phenomenon by analyzing the interaction between artificial intelligence systems and Spanish inclusive strategies in translation into Italian within an augmented translation perspective (Kornacki {\&} Pietrzak 2025). The methodology involved three main steps: data collection, annotation, and analysis. Academic texts originally written in Spanish were gathered from which specific segments were extracted. Using segment-level analysis allowed for the creation of a more diverse corpus. In total, 20 instances were collected for each inclusive language strategy examined: fully split forms, half-split forms, the neomorpheme -e, the typographic sign @ and x. These segments were then translated using four artificial intelligence systems: two neural translation systems (DeepL and Google Translate) and two generative AI systems (ChatGPT and Gemini)."
}