Beyond Obscuration and Visibility: Thoughts on the Different Strategies of Gender-Fair Language in Italian
Authors: Martina Rosola, Simona Frenda, Alessandra Teresa Cignarella, Matteo Pellegrini, Andrea Marra, Mara Floris (Università di Brescia, Università di Torino, Aequa-tech, Università Cattolica del Sacro Cuore, Università Vita-Salute San Raffaele, 2023)
Topic: Gender and Inclusive Language Studies

Abstract
This study focuses on the growing importance of gender-fair language and explores innovative strategies proposed also in other languages to avoid gender-specific endings. We present a set of guidelines for the annotation and reformulation of gender-(un)fair texts and their application to a corpus of 1,024 portions of university administrative documents in Italian. Overall, the guidelines presented in this study prove to be valuable both practically and theoretically. They help identify and address non-inclusive expressions while highlighting the complexities of obscuration and visibility in gender-fair language reformulation. In addition, the statistical analysis of the created corpus shows how administrative texts tend to contain gender-unfair language, especially the masculine overextended expressions, showing the need to adopt specific and complete guidelines that lead (and support the staff training) to the use of a more gender-fair language.

Introduction
Gender-fair language (GFL), also known as inclusive language, refers to the use of linguistic expressions that promote gender equality and avoid reinforcing gender stereotypes. The Italian language, like many others, has traditionally exhibited gender asymmetries and biases, which have perpetuated gender inequality and exclusion. However, in recent years, there has been a growing awareness and effort to address these issues by promoting GFL. In this work, we present the annotation scheme we developed to identify and reformulate gender-unfair expressions, and the corpus we applied it to, encompassing a range of administrative documents of the University of Brescia. The significance of addressing gender-unfair expressions stems from concerns raised by several researchers. In Italian, a personal noun’s grammatical gender typically corresponds to its referent’s gender. In certain cases, however, a discrepancy between the two arises. Crucially, such discrepancies are characterized by an asymmetrical nature, as has been highlighted both from a theoretical and a practical perspective. In Italian, as well as in other languages with gendered nouns, the issue of GFL presents a dual challenge. Firstly, the binary distinction between masculine and feminine in the Italian gender system excludes individuals who identify outside the male-female dichotomy. Consequently, non-binary individuals are consistently misgendered due to the absence of dedicated linguistic forms. Secondly, the usage of the generic masculine (e.g., using masculine profession names to refer to individuals of any gender) and the overextended masculine (referring to mixed-gender groups using the masculine plural) predominantly evoke masculine mental representations, thereby limiting the visibility of female and non-binary individuals. Notably, the presence of a single man in a group is sufficient to alter the gender of the term used to refer to the group, whereas the reverse does not hold true for inverted genders. These discrepancies, in addition to their asymmetrical nature, impact the mental representations we construct. Despite the Italian convention of using masculine terms to refer to individuals of unknown gender and mixed groups, psycholinguistic studies highlight potential issues in this respect. Extensive experimentation conducted over several years using various techniques, and across different languages, suggests that the overextended masculine and generic masculine are interpreted as if they were purely masculine. Recognizing the importance of rectifying such language discrepancies, various guidelines have been published over the years. The annotation scheme we propose draws upon the recommendations presented in the available guidelines, to develop a comprehensive framework for addressing gender-unfair expressions in Italian language usage. To the best of our knowledge, our annotation scheme represents a novel approach. While another project (i.e., E-MIMIC) focuses on inclusive language, it simply distinguishes between inclusive and non-inclusive texts. Our annotation scheme appears to be the first one distinguishing between different types of gender-unfair language, and it comprehensively considers all the gender-fair options when it comes to offering alternative wordings. Moreover, applying this scheme of annotation to various administrative texts, we showed how, despite the existence of various guidelines, they remain impregnated with gender-unfair expressions. In this work, firstly we review previous studies on this topic, both in theoretical linguistics (subsection 2.1) and in NLP (subsection 2.2). We then describe in detail the annotation scheme (section 3) and the creation of the annotated corpus (section 4), also providing a preliminary analysis of the data gathered so far.

Conclusion
In recent years, gender-fair language has gained significant attention, leading to the proposal of new strategies in various languages to avoid using masculine or feminine endings. Motivated by these theories, we conducted a study to examine the usage of different solutions in practical situations. We developed guidelines for gender-fair annotation and reformulation of texts, which we applied to a corpus of 1,024 portions of university administrative documents in Italian. The corpus was annotated by 5 experts, and in 422 cases the annotators identified at least one gender-unfair expression. The preliminary analysis of this corpus highlighted the need to adopt specific guidelines (as well as a list of words to pay particular attention to) to support administrative staff in writing gender-fair texts. Applying our annotation and reformulation guidelines to real data has led to theoretical advancements: we discovered that ‘obscuration’ and ‘visibility’ strategies can coexist within the same reformulation, and we consequently updated the annotation scheme to include ‘mixed’ alternatives. To summarize, the annotation scheme has proven valuable both practically and theoretically. It facilitated the identification of gender-unfair expressions and the formulation of alternatives. Moreover, it revealed the inadequacy of an exclusive distinction between obscuration and visibility, emphasizing the need to incorporate a new type of strategy (i.e., ‘mixed’ alternatives) into the classification. Although the created annotation scheme has been applied only to administrative texts so far, the guidelines are formulated in such a way that they can be easily applied to data pertaining to different domains. Indeed, we plan to extend the annotation to other data, like web pages of a University that describes its organization and its events. Finally, the resulting corpus, composed of 3,195 portions of texts identified as gender-unfair and reformulated with at least one alternative, could be used in the context of training models to recognize gender-unfair expressions and suggest their alternatives.