A Bayesian account of pronoun and neopronoun acquisition
This work addresses the problem of respectful and flexible computational systems for queer people with diverse gender expression, though it is incremental as it builds on existing methods.
The paper tackles the challenge of modeling pronoun and neopronoun acquisition to address equity issues in queer communities by presenting a probabilistic graphical model based on the nested Chinese Restaurant Franchise Process, which accounts for variability in integrating pronouns into symbolic knowledge without relying on lexical co-occurrence statistics.
A major challenge to equity among members of queer communities is the use of one's chosen forms of reference, such as personal names or pronouns. Speakers often dismiss their misuses of pronouns as "unintentional", and claim that their errors reflect many decades of fossilized mainstream language use, as well as attitudes or expectations about the relationship between one's appearance and acceptable forms of reference. We argue for explicitly modeling individual differences in pronoun selection and present a probabilistic graphical modeling approach based on the nested Chinese Restaurant Franchise Process (nCRFP) (Ahmed et al., 2013) to account for flexible pronominal reference such as chosen names and neopronouns while moving beyond form-to-meaning mappings and without lexical co-occurrence statistics to learn referring expressions, as in contemporary language models. We show that such a model can account for variability in how quickly pronouns or names are integrated into symbolic knowledge and can empower computational systems to be both flexible and respectful of queer people with diverse gender expression.