Characterizing non-binary French: A first step towards debiasing gender inference

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.