@inproceedings{chan-etal-2026-word,
    title = "Word Predictability on Code-switching Points in {C}antonese{--}{E}nglish Discourse",
    author = "Chan, Ariel Shuk Ling  and
      Li, Yanting  and
      Poschl, Jacob",
    editor = "Voigt, Rob  and
      Warstadt, Alex  and
      Feldman, Naomi  and
      Linzen, Tal",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2026",
    month = jul,
    year = "2026",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.scil-main.21/",
    pages = "230--243",
    ISBN = "979-8-89176-412-5",
    abstract = "This paper investigates how word predictability influences code-switching probability. We analyze 1,010 code-switched instances drawn from naturalistic sociolinguistic interviews with 41 Cantonese{--}English bilinguals across three bilingual groups (homeland, immersed, and heritage). In particular, we examine whether the predictability of switch points, operationalized as surprisal, influences the likelihood of code-switching. Using pretrained transformer-based language models, we estimate surprisal at the switch point under different modeling conditions, including autoregressive and masked models and varying amounts of contextual information. Mixed-effects logistic regressionanalyses show that less predictable words are more likely to be code-switched. These effects are largely consistent across model types and bilingual groups. Overall, these findings highlight the role of predictability in bilingual speech production and provide new insights into code-switching among bilingual speakers with diverse language experiences."
}