Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender (2022)

Abstract: The world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, Natural Language Processing (NLP) barely reflects this linguistic shift, resulting in the possible exclusion of non-binary users, even though recent work outlined the harms of gender-exclusive language technology. The current modeling of 3rd person pronouns is particularly problematic. It largely ignores various phenomena like neopronouns, i.e., novel pronoun sets that are not (yet) widely established. This omission contributes to the discrimination of marginalized and underrepresented groups, e.g., non-binary individuals. It thus prevents gender equality, one of the UN’s sustainable development goals (goal 5). Further, other identity-expressions beyond gender are ignored by current NLP technology. This paper provides an overview of 3rd person pronoun issues for NLP. Based on our observations and ethical considerations, we define a series of five desiderata for modeling pronouns in language technology, which we validate through a survey. We evaluate existing and novel modeling approaches w.r.t. these desiderata qualitatively and quantify the impact of a more discrimination-free approach on an established benchmark dataset.

## introduction pronouns are an essential component of many languages and often one of the most frequent word classes. accordingly, nlp has long studied tasks related to them, e.g., pronoun resolution (e.g., @xcite . simplistically, they can be defined as "a word (such as i, he, she, you, it, we, or they) that is used instead of a noun or noun phrase". linguistic studies have pointed out the complexity of pronouns, though (e.g., @xcite . pronouns can carry demographic information -in english, for example, information about the number of referents and a single referent's (grammatical) gender. 2 pronouns can convey even more information in other, non-pro-drop languages. 3 consider arabana-wangkangurru, a language spoken in australia, in which a speaker uses different pronouns depending on whether the referent is part of the same social or ritual group (moiety) @xcite . as such, pronouns shape how we perceive individuals and can even reflect cultural aspects (e.g., @xcite and ideologies (e.g., @xcite . consequently, pronoun usage should be considered a sensitive aspect of natural language use. accordingly, in many western societies, these phenomena have been drawing more and more attention. for instance, in 2020, the american dialect society voted "(my) pronouns" as the 2019 word of the year and singular "they" as the word of the decade @xcite . recently, there has been a shift in pronoun usage @xcite , partially due to shifts in the perception of gender, driven by the queer-feminist discourse (e.g., @xcite . related to this is the open discussion of identity beyond binary gender. for instance, a person who does not identify their gender within the gender binary (e.g., a nonbinary or genderqueer person) might use singular "they" as their pronoun. recently, the french dictionary "le robert" added the non-binary pronoun "iel" to its list of words. 4 this "social push" to respect diverse gender identities also affects nlp. recent studies have pointed out the harms of the current lack of non-binary representation in data, embeddings, and tasks (cao and @xcite . however, the research landscape on modern pronoun usage is surprisingly scarce, hindering progress for fair and inclusive nlp. this lacuna is in direct contradiction of the un's sustainable development goals, 5 which include gender equality (goal 5). linguistic research has identified further identity aspects of pronouns, beyond gender @xcite . specifically, nounself pronouns are functionally turning pronouns from a closed into an open word class. to the best of our knowledge, nlp has completely ignored these aspects so far. we did not find a single work systematically describing all of the currently existing phenomena, even just in english 3rd person pronoun usage (let in contrast, in "non-pro-drop languages", pronouns cannot be omitted (e.g., german). ## related work while there are some works in nlp on genderinclusion (e.g., @xcite and gender bias in static (e.g., @xcite @xcite , inter alia) and contextualized (e.g., @xcite @xcite , inter alia) language representations as well as works focusing on specific gender bias in downstream tasks (e.g., @xcite @xcite , we are not aware of any work that deals with the broader field of identity-inclusion. thus, there is no other nlp work that deals with a larger variety of pronouns and acknowledges pronouns as an open word class. for surveys on the general topic of unfair bias in nlp we refer to blodgett et al. (2020) and shah et al. (2020). recently, dev et al. (2021) pointed at the representational and allocational harms (barocas et al., 2017) arising from gender-exclusivity in nlp. they surveyed queer individuals and assessed non-binary representations in existing data sets and embeddings. in contrast, we specifically look at third-person pronoun usage and how to model such phenomena. webster et al. (2018) provide a balanced co-reference resolution corpus with a focus on the fair distribution of pronouns but only focus on the gendered binary case. closest to us, cao and daumé iii (2021) discuss gender inclusion throughout the nlp pipeline beyond binary gender. while they are the first to consider gender-neutral pronouns, including some neopronouns, they do not acknowledge the broader spectrum of identity-related pronoun phenomena. ## a note on identity and pronouns this work focuses on the relationship between identity and pronouns. identity refers to an individual's self-conceptualization, relating to the question of what makes each of us unique @xcite . it can be seen as a two-way process between an individual and others @xcite , and relates to different dimensions, e.g., one's gender. ## phenomena in third-person pronoun-usage we describe existing phenomena and analyze their presence in a collection of threads from reddit. one set of pronouns. each set is possibly reflecting overlapping or non-overlapping aspects of their identity. we provide examples of these sets in ## how can and should we model pronouns? we devise five desiderata based on our observations, personal experiences, expert knowledge from interactions with lgbtqia+ associates, and informal discussions with individuals using genderneutral pronouns. we validate the desiderata through a survey. here, we collect opinions from 39 individuals (149 in the pre-study), most of whom identify as non-binary. we then assess how well classic and novel nlp pronoun modeling paradigms fulfill the criteria. ## the effect of delexicalization in §5.3, we discussed delexicalization, i.e., not modeling lexical forms of pronouns, as one way to counter exclusion in statistical modeling and bucketing. a possible counter-argument against this approach is that omitting the surface forms will lead to poor performance on pronoun-related tasks. we experimentally quantify the loss from (fairer) delexicalization in co-reference resolution. ## conclusion this work provides an initial overview of the plethora of current phenomena in 3rd person pronoun usage in the english language. for practical and ethical reasons, the nlp community should acknowledge the broad spectrum of possible identities and the respective manifestations in written and oral communication. language is consistently evolving, and nlp researchers and practitioners should account for this to provide genuinely inclusive systems. notably, pronouns, traditionally handled as a closed class of words, seem to function closer to an open class. based on the observations from our literature search, research in non-academic, publicly-available writing, a corpus study, and a survey, we defined five desiderata. we validated those and applied them to the discussion of existing and novel modeling paradigms. our findings raise the questions when and how to model pronouns and whether and how to include users in these decisions. with this work, we hope to start a broader discussion on the topic. our study can inform future nlp research and serve as a starting point for creating novel modeling procedures. all code needed to reproduce our experiments is publicly available at https: //github.com/anlausch/pronouns .