Gender-inclusive language and machine translation: from S panish into I talian (2025)

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 niños y las niñas, 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 (ə) 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).

## introduction direct 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 @xcite . spanish uses split forms and gender doublets (los niños y las niñas, los/as candidatos/as), the neomorpheme -e, and typographic signs such as @ and x (román 2025, papadopoulos 2022). similarly, italian employs split forms and gender doublets (i bambini e le bambine, i/le candidati/e), the schwa (ə) as a neomorpheme, and the asterisk (*) as a typographical symbol. while gender doublets and the @ sign aim to make women visible within a binary framework, the others are intended to give visibility to non-binary gender identities as well (escandell-vidal 2020, giusti 2022). in the augmented translation era, it is crucial to study and evaluate the performance of automatic translation systems to determine whether and to ________________________________________________ © 2025 the authors. this article is licensed under a creative commons 4.0 licence, no derivative works, attribution, cc-by-nd. what extent diverse gender representations are maintained in the process of translation @xcite ). ## methodology the methodology involved three main steps: data collection, annotation, and analysis. academic texts originally written in spanish were gathered; from those specific segments were extracted. segment-level analysis allowed for the creation of a more diverse corpus. in total, approximately 20 instances were collected for each inclusive language strategy examined: split forms, doublets, the neomorpheme -e, the sign @ and the letter 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). additionally, two prompts were created for the last two systems: a complex one (pc) and a simple one (ps). based on the few-shot prompting technique, the former (pc) included information about the translation task, namely (i) source and target languages, (ii) intended purpose (publication) and (iii) examples to guide the system. specifically, it provided examples linking source strategies with corresponding target strategies. the latter (ps) included the same information, except for the examples. the translated outputs were annotated using uamcorpustool (o'donnell 2008), and a taxonomy of effects was created. this taxonomy categorizes translation outcomes into several effects such as: same level of inclusivity, with further subdivisions that indicate whether this was achieved through a (non)standard linguistic strategy or because the equivalent in the tl is neutral or a common-gender noun; overtranslation, which marks all outputs that contain unnecessary inclusive language marks; gender-inclusive language and machine translation: from spanish into italian antonella bove università ca' foscari venezia antonella.bove@unive.it misinterpretation refers to erroneous interpretation of the machine with a consequent meaning error; elimination of inclusive markers that were used in the source text (but without omitting the word itself); shift in the level of inclusivity, from binary to non-binary and vice versa, changing representations arbitrarily; morphological error concerns the target text, as it refers to cases in which the system uses a gender-inclusive mark without respecting the morphological constraints of the target language; inconsistency refers to the use of different gender-inclusive marks within the same segment; untranslated label refers to untranslated words; agreement error is for cases in which the system uses a gender-inclusive mark that does not agree with the other words in the phrase; loan word/anglicism concerns the translation of a word with a loan word from a language without grammatical gender, such as english, making it more inclusive. ## results the analysis revealed significant differences between the generative and non-generative systems. deepl and google translate tended to eliminate gender inclusive marks, including split forms and doublets, defaulting to masculine forms. on the contrary, chatgpt and gemini demonstrated a better capacity to maintain inclusivity but did so inconsistently. moreover, these systems generated syntactical and morphological errors in some cases, likely due to inadequate training on direct gender inclusive language forms. additionally, there were numerous instances of arbitrary shifts in the gender marking, from binary to non-binary and vice versa, significantly altering the represented identities. finally, few-shot prompting resulted in better outputs compared to basic prompting, suggesting an interaction between prompt accuracy and translation accuracy. ## conclusion & future work the study concludes that generative ai systems show greater potential for the translation of genderinclusive texts when guided by well-designed prompts, compared to neural machine translation systems that are not effective for this purpose as they tend to eliminate almost all the direct strategies. however, two key methodological limitations should be acknowledged, due to limited resources and the pioneer nature of the study: a) the annotation was conducted by a single annotator, preventing the calculation of the inter-annotator agreement and therefore limiting empirical validation and b) the analysis was carried out on a segment level, which, while useful for isolating specific linguistic phenomena, does not account for broader discourse-level or co-textual influences that could affect translation outputs. future research should involve multiple annotators and adopt a document-level approach to enhance the reliability and generalizability of the findings.