@inproceedings{flesch-burnett-2025-characterizing,
    title = "Characterizing non-binary {F}rench: A first step towards debiasing gender inference",
    author = "Flesch, Marie  and
      Burnett, Heather",
    editor = "Fale{\'n}ska, Agnieszka  and
      Basta, Christine  and
      Costa-juss{\`a}, Marta  and
      Sta{\'n}czak, Karolina  and
      Nozza, Debora",
    booktitle = "Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.gebnlp-1.16/",
    pages = "160--170",
    ISBN = "979-8-89176-277-0",
    abstract = "This paper addresses a bias of gender inference systems: their binary nature. Based on the observation that, for French, systems based on pattern-matching of grammatical gender markers in ``I am'' expressions perform better than machine-learning approaches (Ciot et al. 2013), we examine the use of grammatical gender by non-binary individuals. We describe the construction of a corpus of texts produced by non-binary authors on Reddit, (formely) Twitter and three forums. Our linguistic analysis shows three main patterns of use: authors who use non-binary markers, authors who consistently use one grammatical gender, and authors who use both feminine and masculine markers. Using this knowledge, we make proposals for the improvements of existing gender inference systems based on grammatical gender."
}