G e NR e: A F rench Gender-Neutral Rewriting System Using Collective Nouns (2025)

Abstract: A significant portion of the textual data used in the field of Natural Language Processing (NLP) exhibits gender biases, particularly due to the use of masculine generics (masculine words that are supposed to refer to mixed groups of men and women), which can perpetuate and amplify stereotypes. Gender rewriting, an NLP task that involves automatically detecting and replacing gendered forms with neutral or opposite forms (e.g., from masculine to feminine), can be employed to mitigate these biases. While such systems have been developed in a number of languages (English, Arabic, Portuguese, German, French), automatic use of gender neutralization techniques (as opposed to inclusive or gender-switching techniques) has only been studied for English. This paper presents GeNRe, the very first French gender-neutral rewriting system using collective nouns, which are gender-fixed in French. We introduce a rule-based system (RBS) tailored for the French language alongside two fine-tuned language models trained on data generated by our RBS. We also explore the use of instruct-based models to enhance the performance of our other systems and find that Claude 3 Opus combined with our dictionary achieves results close to our RBS. Through this contribution, we hope to promote the advancement of gender bias mitigation techniques in NLP for French.

## introduction since the 1970s, several psycholinguistic studies have shown how language influences thoughts @xcite . further studies examining gender in language proved that it could lead to cognitive biases @xcite , particularly when it comes to the use of masculine generics @xcite , that is masculine words that are supposed to refer to mixed groups of men and women or to someone who gender is unknown @xcite @xcite . for example, @xcite showed that when asked to name a celebrity in a certain field in german, respondents were more likely to give the name of a man when a mg was used in the question. gender bias in natural language processing (nlp) models is a critical issue that can lead to biased predictions and the amplification of bias in the training data @xcite @xcite . this problem is particularly relevant for machine translation systems, which are highly susceptible to gender bias when translating between languages with different grammatical gender systems @xcite @xcite . data augmentation, which involves balancing the amount of data for all genders in a specific language, has been suggested as a potential solution to debias nlp systems @xcite . this led to an nlp task known as "gender rewriting," whose goal is to automatically propose alternatives to gendered sentences. as of yet, automatic gender neutralization techniques have only been developed in english ( @xcite . in french, the only gender rewriting system created is, to our knowledge, that of @xcite , which converts mg to gender-fair forms highlighting the feminine suffix foot_0 . thus, we develop a french gender-neutral rewriting system using human collective nouns (cn), defined by @xcite as "nouns referring to entities comprised of groups of individuals." 2 cns have been widely discussed in the literature, especially when it comes to french @xcite @xcite . since, in french, this type of noun has a gender which does not depend upon the referent's, 3 it is an effective way of achieving gender neutralization. by focusing on gender neutralization, our work targets a writing technique that, compared to visibilization (used by @xcite ), tends to be less contentious among native french speakers, as it does not alter the spelling of existing words nor does it introduce non-standard or new punctuation marks to separate the feminine suffix from the base word form @xcite . finally, gender neutralization challenges the binary male/female gender dichotomy and is better adapted for people whose gender falls outside of the traditional categories. our gender-neutral rewriting system, genre (gender-neutral rewriting system using french collective nouns), is the very first genderneutral rewriting system for french 4 . we present three different versions of our system: one relying on a rule-based system (rbs), one relying on fine-tuned language models and one relying on an instruct-based model. ## gender in french in french, nouns (n) are classified as either masculine or feminine, and the gender of a noun influences the form of determiners (d), adjectives (a) and past participle verbs (v) that are syntactically related. similarly, coreferent pronouns (p), that is pronouns referring to a previously mentioned entity, also feature the same gender. examples 1 (masculine) and 2 (feminine) highlight the syntactic differences that arise when using either an inanimate masculine noun ("courrier", mail) and an inanimate feminine noun ("lettre", letter). the gender of human role nouns reflects the sociological gender of the referent and is motivated (for instance, "danseuse" refers to a female dancer), while gender of nouns referring to unanimated beings is arbitrary @xcite . the masculine gender for human nouns is considered to be the "default" gender in french, and can be used in a non-specific context (in the singular form, as in example 3 foot_2 ) or to refer to groups of people composed of both men and women (in the plural form, as in example 4). however, the use of masculine as the default gender (masculine generics, abbreviated mg) in gender-marked languages promotes androcentric mental representations and participates in invisibilizing women @xcite . a 2017 survey conducted by harris @xcite among french speakers found that respondents tend to have male-centric representations when mg are used in the questions. similarly, according to @xcite , mg human nouns are more likely to be associated with male referents, and specifically highlighting the generic nature of mg does not have an effect on the biased perception of survey participants @xcite . consequently, two main types of writing techniques can be used to avoid the use of mg: visibilization techniques and neutralization techniques. visibilization techniques seek to highlight the feminine ending of words by separating the masculine ending from the feminine one through the use of specific symbols (asterisk, interpunct: professeur•e, as in example 5) or by affecting the feminine ending directly (using capital or bold letters). neutralization techniques, on the other hand, mainly revolve around various types of words: epicene words, that is words whose form is the same for masculine and feminine, whether they may have a generic (e.g. "personne", person, as in example 6) meaning or a specific (e.g. "spécial-iste", specialist) one, or words that refer to groups of people, such as cns (e.g. "lectorat", readership, as in example 7), which we use for this work. cns have a fixed gender which is not associated with the genders of the people within that group. given the impact of inclusive formulations on mitigating mg-induced gender biases @xcite @xcite , developing a system to automatically rewrite text and reduce the prevalence of mg could be a valuable tool for data augmentation. by focusing on gender neutralization, our work aims to fill this gap and explore the potential of cns and epicene words in promoting more inclusive language. while gender rewriting works focusing on automatic gender neutralization do exist in english @xcite @xcite , no such efforts have been pursued for french. similarly, studies about gender neutralization and its application to nlp tools exist in italian @xcite and german @xcite , but gender neutralization in nlp for french has not yet been addressed. ## the task of gender rewriting in order to mitigate gender bias in nlp textual data, various systems have been developed to rebalance or debias datasets by generating alternative formulations when it comes to the use of grammatical gender. while @xcite were the first to define this task as "gender rewriting," similar efforts had already been pursued for arabic @xcite , german @xcite , and english @xcite . @xcite define this task as: "generating alternatives of a given arabic sentence to match different target user gender contexts." while this definition works well for the work pursued by @xcite , as they focus specifically on arabic and create a system to switch between the masculine gender and the feminine gender, it is not universally applicable. indeed, among the aforementioned works, several approaches to gender rewriting have been explored: habash et al. (2019) and alhafni et al. (2022a) developed a system to transform arabic sentences with masculine words into sentences with feminine equivalents, and vice versa. the system created by @xcite provides inclusive suggestions for input sentences in german and has led to the publication of an online resource letting the user choose the type of inclusive transformation to apply. more recently, @xcite also developed an inclusive gender-rewriting system for portuguese, and @xcite for french. finally, @xcite , @xcite and @xcite created systems to neutralize gender in an english input sentence, but no such system exists for french. as part of this work and in order to accommodate a larger number of languages and transformation types, we reframe the initial task definition given by @xcite as "generating one or more alternative sentences that either neutralize gender, adopt inclusive forms, or switch to a different gender". ## methodology to build our automatic gender neutralization system, we propose three different approaches: a rulebased approach, a model fine-tuning approach, and an instruct-based model approach. we first create a dictionary of french cns and their member noun counterparts, which will be used to make the necessary gender transformations. we describe this dictionary in section 4.1. in section 4.2, we then give details about the datasets that we extracted sentences from for language model (lm) fine-tuning from rbs data, and evaluation. finally, in section 4.3, we explain our experimental design with the aforementioned model types. while our work focuses specifically on french, the methodology presented below is applicable to any language which can use collective nouns as a gender-neutralizing technique (e.g., spanish) given a dictionary of human-member nouns being already available or being created following our methodology. when it comes to syntactic changes, especially considering gender and number, those would be very similar in languages with similar inflection, such as spanish, italian or portuguese. as a result, the amount of work needed to adapt our methodology to these languages specifically would be much lower compared languages encoding number or gender differently. ## discussion we conduct a quantitative and qualitative analysis of errors made by each model type. this analysis is done manually for the rbs and fine-tuned models, and automatically for the instruct-based model. model model model model count morphosyntax collective nouns and (co)reference semantics other 15 4 1 169 143 163 • adj • det • prep • verb 152 126 40 44 51 48 • det_coref • misid_noun • pron_coref • unreplaced • invalid_collnoun • specificity 106 238 73 79 80 78 • sem 88 63 14 23 61 86 • case • elision • gen_failure • punct • special_char • redundancy • tone_change • unfaithful the latter method is chosen because llms tend to be significantly more permissive in their generations, resulting in a broader range of error types that would complicate manual annotation. we define four high-level categories of errors, as illustrated in figure 2 : morphosyntax; collective nouns and (co)reference; semantics; and other. for genre-rbs, genre-t5, and genre-m2m100, errors were manually annotated across both corpora and verified by two annotators. annotation details are available in appendix d.1. based on the outputs from the rbs and fine-tuned models, we established 14 subcategories of errors (italicized in figure 2 ), which were then applied to each sentence generated by each model (500 sentences × 3 models). multiple error labels can be assigned to a single sentence. descriptions of each error type can be found in for claude 3 opus and its instructions (base, dict, corr), we use the "llm-as-judge" approach @xcite to automate error annotation using gpt-4o mini (openai, 2024) and in-context learning @xcite . first, gpt-4o mini is prompted to provide a brief explanation of the error(s) in each generated sentence compared to the reference. we generate 10 different outputs and identify the most frequent explanation by comparing sentence-level embeddings using sbert @xcite and sentence-camembert-large foot_8 . subcategories of errors are then assigned to each sentence based on these explanations and the manually defined taxonomy. the most frequent subcategory across the 10 generations is retained. prompts are included in appendix e. we validate these automatically assigned subcategories by reviewing selected model outputs to ensure consistency, filtering out irrelevant or incorrectly attributed labels, and merging valid labels with those used in manual evaluation. figure 2 shows the distribution of errors across models, grouped into the four main categories. morphosyntactic errors are most frequent in the rbs model (169 instances), though slightly reduced in the fine-tuned models (163 for m2m100, 143 for t5). in both cases, errors involving adjectives (adj) and verbs (verb) are most prevalent. this is not surprising given that these two part-ofspeech categories are the ones which require the most complex changes when transitioning from a member noun to a cn. indeed, in french, adjectives undergo a certain number of changes when changing number or gender. verbs can also have these same changes when used as past participles; otherwise, only number change will affect them. for instance, in example 9a, the verb "veulent" (pl., want) was correctly changed by m2m100 to "veut" (sg.) to match with the new cn "actorat" (group of actors). thus, despite their overall lower performance, these models show promise for correcting such errors. the instruction-tuned model variants exhibit significantly fewer such errors, reflecting their great linguistic capabilities. for collective noun and (co)reference errors, the "invalid_collnoun" label denotes the failure to use a valid collective noun present in the reference sentence: it is typically replaced by a mg (example 9b). the "specificity" error captures shifts in noun specificity, such as changes from definite to indefinite forms. semantic errors are treated as a distinct category due to their frequency. all models tend to struggle with these, likely because the use of collective nouns is governed by strict semantic rules that limit their applicability in many contexts. the base instruction model exhibits a notably higher number of such errors (238), probably due to the increased generative freedom permitted by the prompt, which leads to the use of inappropriate or non-existent collective nouns (example 9d). when it comes to other types of errors, errors observed in the fine-tuned models and different from the rbs included token generation failures (m2m100 mostly, example 9e, where *"nebski" was generated instead of "zemski"), and incorrect generation of special characters (t5 only, as in example 9f where *"main-d'uvre" was generated instead of "main-d'oeuvre" @xcite ). the first error might come from the multilingual aspect of the model, as it may generate words or mix tokens from other languages, while the second error is probably due to the model being mostly trained on english data. for both models, we also found cases where words were not uppercased correctly, as in example 9g. additional examples and their translations can be found in appendix f. ([. . . ] the support given to the commmission of the local group of actors who (want [pl.] rbs | want [sg.] m2m100 ) to participate [. . . ]) b. le point culminant est une attaque contre (la rébellion cachée orig | les rebelles cachés opus-corr ) dans les montagnes [. . . ] (the climax is an attack against (the rebellion hidden [fem. sg.] orig | the rebels hidden [masc. pl.] opus-corr ) in the mountains [. . . ]) c. l'armée nigérienne déplore également la perte d'un blindé type 92a, détruit par (les soldats nigériens orig | l'armée nigérienne rbs ), de 6 autres véhicules armés, de deux canons de 122mm [. . . ] (the nigerian army also deplores the loss of a 92a armored vehicle, destroyed by (nigerian soldiers orig | the nigerian army rbs ), as well as 6 other armed cars, two 122-mm cannons [. . . ]) d. en 1531, pour payer sa dette (aux marchands orig | au négoce opus-base ) de lübeck, le roi gustav vasa [. . . ] (in 1531, to pay his debt (to the dealers orig | to the business opus-base ) of lübeck, the king gustav vasa [. . . ]) e. juin, russie : le (zemski orig | nebski m2m100 ) sobor prend des décisions importantes. (june, russia: the (zemski orig | nebski m2m100 ) sobor makes important decisions.) f. il est allé à cologne, où il est devenu président de l'association de la (maind'oeuvre orig | main-d'uvre t5 ) [. . . ] ((he went to cologne, where he became president of the labour organization [. . . ]) g. (l' orig | l' t5 )armée arriva avec une lance à eau pour disperser les détenus. ((the orig | the t5 ) army arrived with a water hose to disperse the prisoners.) ## conclusion our work represents a step towards addressing gender-biased textual data in french. we make three key contributions to the task of gender rewriting in nlp: 1) a dictionary of french cns and their corresponding member nouns, which serves as a resource for future research in this area; 2) a dataset of gender-neutralized and non-gender-neutralized sentences; and 3) a rule-based system that effectively gender-neutralizes french sentences using cns. our experiment combining our manually created dictionary with the claude 3 opus instructbased model also shows promise for the use of such models for the task of gender rewriting. we believe that future research further exploring the capabilities of these models for that task could lead to the development of effective solutions for mitigating gender bias in other languages with collective nouns (such as spanish) or similar gender neutralization techniques.