A Rewriting Approach for Gender Inclusivity in P ortuguese (2023)

Abstract: In recent years, there has been a notable rise in research interest regarding the integration of gender-inclusive and gender-neutral language in natural language processing models. A specific area of focus that has gained practical and academic significant interest is gender-neutral rewriting, which involves converting binary-gendered text to its gender-neutral counterpart. However, current approaches to gender-neutral rewriting for gendered languages tend to rely on large datasets, which may not be an option for languages with fewer resources, such as Portuguese. In this paper, we present a rule-based and a neural-based tool for gender-neutral rewriting for Portuguese, a heavily gendered Romance language whose morphology creates different challenges from the ones tackled by other gender-neutral rewriters. Our neural approach relies on fine-tuning large multilingual machine translation models on examples generated by the rule-based model. We evaluate both models on texts from different sources and contexts. We provide the first Portuguese dataset explicitly containing gender-neutral language and neopronouns, as well as a manually annotated golden collection of 500 sentences that allows for evaluation of future work.

## introduction the relationship between language and gender, as well as its effects on societal gender dynamics, has been examined and documented as early as the 70s @xcite . in more recent years, there has been a push towards the usage of gender-neutral (or gender-fair) language. how this gender-fairness is achieved, is, however, highly dependent on the specifics of each language. during the course of this work, we will concern ourselves with the case of portuguese. like most romance languages, portuguese is characterized by a binary grammatical gender system in which nouns belong to one of two classes: masculine or feminine. during the last decade, with the advent of social media, there has been an increase in the visibility and usage of new sets of gender-neutral pronouns (pinheiro, 2020) (sometimes referred to as neo-pronomes in portuguese). these neopronouns are preferred by many individuals to refer to themselves @xcite , but can also be used to refer to a mixed-gender group of people avoiding the default masculine plural -for example, um grupo de alunes instead of um grupo de alunos (en: a group of students). although the lack of gender inclusion in nlp datasets is a multilingual problem @xcite , most efforts being made towards the processing of gender-neutral language are focused on germanic languages @xcite @xcite . as such, we set out to create nlp models that allow for the processing of portuguese gender-neutral language in the same way that is currently being achieved for other languages. the three main contributions of our work consist of: as previously mentioned, our system falls into the category of what we will call gender-neutral rewriter systems. these rewriters take as input some form of gendered text and output a genderneutral version of that same text. what is considered to be gender-neutral greatly varies between proposed systems and languages. ## grammatical gender and gender-neutral language in portuguese like most romance languages, portuguese has two genders: masculine (characterized by an -o termination in gendered terms) and feminine (characterized by an -a termination in gendered terms). most of the current proposals for a third neural gender for portuguese are characterized by an -e or an -u termination in gendered terms. these proposals tend to arise in the form of practical guides, both by brazilian and european portuguese authors, and tend to be based on informal studies and observations of the neutral language used by queer communities. since the usage of gender-neutral language in portuguese is an ongoing debate, in this section we present some guides reporting on the community usage of gender-neutral language. ## datasets our dataset is split into five text categories: literary texts selected works found in the dip collection foot_1 from linguateca foot_2 resource center. dip is a shared task whose goal is to identify of characters and respective attributes in literary works @xcite . in order to avoid examples with an orthography that might be too different from modern portuguese, we have only selected works released after 1910. cleaning and processing the data consisted of the removal of tags (such as chapter indications, language tags, etc.) and author notes. journalistic texts random sample of sentences found in the naturapublico94 dataset, from projecto natura foot_3 . the original corpus contains the first 2 paragraphs of each article in the portuguese newspaper público, retrieved during the period of 1991 to 1994. naturapublico94 was retrieved during 1994. dialogues random sample of extracted sentences from the subtle corpus @xcite . subtle aggregates dialogues from movie subtitles, extracted from imdb foot_4 and pertaining to one of four movie genres: horror, scifi, western, and romance. dur-social media random sample of tweets from the portuguese tweets for sentiment analysis foot_5 dataset, which contains examples retrieved mainly from 01/08/2018 to 20/10/2018. we only used tweets from the "no theme" partition of the dataset. we removed links, mentions, hashtags, and emojis. simple sentences samples from the portuguese dataset of the tatoeba (tiedemann, 2020) corpus, consisting of relatively simpler (both in terms of vocabulary and syntactic construction) and less noisy sentences. we chose to divide the data into five categories to analyze the performance of our models in different types of text, and how they account (or not) for variability of sentence structure and vocabulary. ## experiments 6.1 metrics due to the novelty of the task of gender-neutral rewriting, we find that there is a lack of general consensus on useful metrics for evaluating these types of rewriter systems. for comparison with the works described in section 2, we employ wer and bleu-4 @xcite (usually the default n-gram value for bleu scores) as metrics. since genderneutral language in romance languages often consists in a single character change (e.g. "joão é bonito." @xmath0."), we also employ cer (character error rate) as a metric. additionally, we calculate bleu-1 and rouge-1 @xcite . ## conclusion in this paper, we present the first portuguese dataset explicitly containing gender-neutral language, along with a rule-based and a neural genderneutral rewriters. additionally, we provide a manually annotated collection of 500 original sentences and a respective gender-neutral version. one entry of our automatically curated dataset (used for training the nm) consists of a binary-gendered sentence with the respective gender-neutral version provided by the rbm. we provide the first benchmarks of the gender-neutral rewriting task for the portuguese language. although the neural model can generalize over the seen data, we hypothesize that it fails to internalize the context of sentences and whether gendered terms refer to humans or objects. in future work, we hope to bypass this issue either by training a larger model with more quality data, and/or developing a hybrid rule-based/neural model. another possible approach is data augmentation using llm prompting. in a first attempt to replicate this approach, we have prompted chat-gpt foot_12 , the sibling model to instructgpt @xcite , to generate portuguese gender-neutral sentences. results are depicted in appendix d. although a promising approach, we hypothesize that the generated sentences do not have the necessary consistency and variety to create a quality dataset. we expect that, in the future, with the advancement