Automating Gender-Inclusive Language Modification in Italian University Administrative Documents

In this work, we address the issue of automating the identification of non-inclusive language in administrative documents of Italian universities as well as providing gender-inclusive corrections. To achieve this objective, data from various Italian universities were gathered, leading to the creation of a dictionary containing potentially non-inclusive terms, and of a dataset containing gender non-inclusive sentences and their corresponding inclusive versions. Subsequently, three distinct approaches have been defined and evaluated: a rule-based and two neural approaches. In the development of the rule-based approach, Italian Part-of-Speech tagging, dependency parsing, and morphologization techniques were employed to detect masculine trigger words within sentences, ascertain whether they functioned as generic masculine terms, and offer gender-inclusive alternatives. In contrast, for the implementation of the two neural approaches, both the mT5 model and ChatGPT were utilized, and their respective outputs were compared against the rewritten sentences they generated. The experimental evaluations conducted suggest the effectiveness of the proposed solutions.