How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns (2022)

Abstract: Gender-neutral pronouns have recently been introduced in many languages to a) include non-binary people and b) as a generic singular. Recent results from psycholinguistics suggest that gender-neutral pronouns (in Swedish) are not associated with human processing difficulties. This, we show, is in sharp contrast with automated processing. We show that gender-neutral pronouns in Danish, English, and Swedish are associated with higher perplexity, more dispersed attention patterns, and worse downstream performance. We argue that such conservativity in language models may limit widespread adoption of gender-neutral pronouns and must therefore be resolved.

## introduction many linguistic scholars have observed how technology in general has altered the course of language evolution @xcite , e.g., through the influence of social media conventions. language technologies, in particular, have also been argued to have such effects, e.g., by reducing the pressure to acquire multiple languages. gender-neutral pronouns is not an entirely modern concept. in 1912, ella flag young, then superintendent of the chicago public-school system, said the following to a room full of school principals: "the english language is in need of a personal pronoun of the third person, singular number, that will indicate both sexes and will thus eliminate our present awkwardness of speech." the use of gender-neutral pronouns has become much more popular in recent years @xcite . in 2013, a gender-neutral pronoun was politically introduced in swedish @xcite which can be used for both, people identifying outside the gender dichotomy and as a generic pronoun where information about gender is either unavailable or irrelevant. in a recently recorded eye-tracking study, @xcite found no evidence that native speakers of swedish find it harder to process gender-neutral pronouns than gendered pronouns, an argument often brought up by opponents of gender-inclusive language (speyer and @xcite . in combination with their increasing popularity, this suggests gender-neutral pronouns have been or will be widely and fully adapted over time @xcite . however, since language technology has the potential to alter the course of language evolution, we want to make sure that our nlp models do not become a bottleneck for this positive development. ## model perplexity and attention in this section we introduce a swedish eye-tracking study and explain how we adapt this study to investigate gender-neutral pronouns in language models. he/they would have visitors in the evening. it has recently been shown that attention flow, in contrast to attention itself, correlates with human fixation patterns in task-specific reading @xcite . we applied a similar analysis pipeline here and extracted all 384 sentence pairs and fed them into the uncased swedish bert model. 2 we calculate perplexity values for each sentence pair over word probabilities as given by bert with the formula proposed by @xcite . furthermore, we calculate attention flow @xcite propagated from layers 1, 6 and 12 and extract attention flow values assigned to the pronoun with respect to the entity. attention flow considers the attention matrices as a graph, where tokens are represented as nodes and attention scores as edges between consecutive layers. the edge values, i.e., attention scores, define the maximal flow possible between a pair of nodes. we consider different parameters of human fixation which we assume might be influenced by a change in pronouns, in particular during pronoun resolution, i.e., first and total fixation time on the pronoun and fixation time after the first fixation on the noun. for both attention flow and perplexity, however we could not find any meaningful correla-tion to those parameters. one reason for that might be that the dataset only contains fixations for the two entities, i.e., pronoun and noun, which makes data comparably sparse and impossible to extract complete reading patterns. ## downstream tasks we also perform downstream task experiments on natural language inference and coreference resolution for both gendered and gender-neutral pronouns to investigate to what extent gender-neutral pronouns influence the performance. natural language inference natural language inference (nli) is commonly framed as a classification task, which tests a model's ability to understand entailment and contradiction @xcite . despite high accuracies achieved by sota models, we are yet to know whether they succeed in combating gender bias, especially in crosslingual settings. we apply two multilingual models mbert foot_5 @xcite and xlm-r foot_6 (conneau et al., 2020) with cross-lingual fine-tuning, i.e., we fine-tune on english and apply both models also on danish and swedish. therefore, mbert was fine-tuned on the english mnli train split and evaluated on xnli. for xlm-r, we apply a model that has been fine-tuned on both mnli and anli @xcite 8 . for english we test both models on the mnli test split, for danish and swedish we test on the extended xnli corpus @xcite , the manual translation of the first 15000 sentences of the mnli corpus @xcite from english into 15 languages. ## related work more eye-tracking studies have been conducted investigating the influence in processing cost for both gender-neutral pronouns and the generic male pronoun. irmen (2007) and @xcite find male biases when using generic male pronouns in dutch and generic role nouns in german. the authors of @xcite found a clear processing cost when using singular they in english, however their stimuli did not include any investigation of how (anti-)stereotypes influence this processing cost and is thus only in parts comparable to other studies. english datasets have been proposed to investigate gender bias in pronoun resolution but have not reported on performance differences between gendered and gender-neutral pronouns @xcite @xcite . @xcite propose a rewriting task where data is transferred from gendered to gender-neutral pronouns to train more inclusive language models. cao and daumé iii (2020) and @xcite discuss the necessity of including non-binary pronouns into nlp research (see also @xcite ). ## discussion with this paper we provide a first study on how well language can handle gender-neutral pronouns in danish, english and swedish for various tasks. we observe an increase in perplexity for genderneutral pronouns and correlations between perplexity on sentence level and attention flow on the pronoun, in particular for english and swedish that gets stronger across layers. this indicates that language models indeed struggle with the use of gender-neutral pronouns, even with singular they, which has been used for many years as genderneutral (saguy and williams, 2022). the reason for this most likely lies in the sparse representation of gender-neutral pronouns in the training data and the fact that language models, once they are trained and published usually are not updated @xcite . however, transformer models pre-trained on subword units have been shown to be robust with respect to word frequency @xcite and thus should be able to process unfamiliar gender-neutral pronouns. at the same time, we observe that word representations of all swedish 3rd person pronouns grow closer in middle and top layers (see figure 1 ) which suggests that relevant information is also learned for genderneutral hen. for nli, we only see a small drop in performance when exchanging gendered pronouns for gender-neutral pronouns which is in the same range as a baseline analysis where we exchange punctuation ("!" for "."), except for danish høn. we argue that classification in nli probably does not heavily rely on individual pronouns in most cases. in stark contrast to pronoun resolution where we see a very clear drop in performance for english when applying singular they in comparison to both female and male pronouns, again this is surprising since in theory language models should have seen training samples where singular they has been used. the small drop in performance for danish coreference resolution might be because this dataset does not solely focus on pronoun resolution, though further investigation is needed here. we strongly argue that more needs to be done to adapt language models to a more gender inclusive language, initiatives like the rewriting task as proposed by @xcite need to be implemented and extended.