@inproceedings{jacobs-grobol-2025-bayesian,
    title = "A {B}ayesian account of pronoun and neopronoun acquisition",
    author = "Jacobs, Cassandra L  and
      Grobol, Morgan",
    editor = "Pranav, A  and
      Valentine, Alissa  and
      Bhatt, Shaily  and
      Long, Yanan  and
      Subramonian, Arjun  and
      Bertsch, Amanda  and
      Lauscher, Anne  and
      Gupta, Ankush",
    booktitle = "Proceedings of the Queer in AI Workshop",
    month = may,
    year = "2025",
    address = "Hybrid format (in-person and virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.queerinai-main.5/",
    pages = "35--40",
    ISBN = "979-8-89176-244-2",
    abstract = "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 dimiss errors in pronominal use as unintentional, and claim that their errors reflect many decades of fossilized mainstream language use, including attitudes or expectations about the relationship between one{'}s appearance and acceptable forms of reference. Here, we propose a modeling framework that allows language use and speech communities to change over time, including the adoption of neopronouns and other forms for self-reference. We present a probabilistic graphical modeling approach to pronominal reference that is flexible in the face of change and experience while also moving beyond form-to-meaning mappings. The model critically also does not rely on lexical covariance structure to learn referring expressions. We show that such a model can account for individual differences 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."
}