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

TL;DR

This paper discusses the evolving landscape of pronouns, emphasizing the need for NLP models to inclusively represent diverse identities, including neopronouns, to reduce discrimination and better reflect linguistic realities.

Contribution

It introduces desiderata for pronoun modeling in NLP, evaluates existing and new approaches qualitatively, and quantifies the benefits of inclusive models on benchmark data.

Findings

Existing models often ignore neopronouns and marginalized identities.

Inclusive pronoun modeling reduces discrimination in NLP applications.

Novel approaches better align with ethical considerations and linguistic diversity.

Abstract

The world of pronouns is changing. From a closed class of words with few members to a much more open set of terms to reflect identities. However, Natural Language Processing (NLP) is barely reflecting this linguistic shift, even though recent work outlined the harms of gender-exclusive language technology. Particularly problematic is the current modeling 3rd person pronouns, as it largely ignores various phenomena like neopronouns, i.e., pronoun sets that are novel and not (yet) widely established. This omission contributes to the discrimination of marginalized and underrepresented groups, e.g., non-binary individuals. However, other identity-expression phenomena beyond gender are also ignored by current NLP technology. In this paper, we provide an overview of 3rd person pronoun issues for NLP. Based on our observations and ethical considerations, we define a series of desiderata for modeling pronouns in language technology. We evaluate existing and novel modeling approaches w.r.t. these desiderata qualitatively, and quantify the impact of a more discrimination-free approach on established benchmark data.