@inproceedings{zheng-xiong-2026-thesis,
    title = "Thesis Proposal: Diagnosing and Mitigating Semantic Interference in Script-Sharing Low-Resource Language Models: A Case Study on Square {B}ai Script",
    author = "Zheng, Jingting  and
      Xiong, Deyi",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.43/",
    pages = "487--497",
    ISBN = "979-8-89176-393-7",
    abstract = "Multilingual language models now cover more languages than ever, yet script-sharing low-resource languages remain vulnerable to failures driven by script and dominant-language priors. This dissertation studies one such failure mode, $semantic$ $interference$, in Square Bai Script, where many forms resemble Chinese characters but often differ in meaning. We argue that current adaptation pipelines underperform not only because Bai is low-resource, but because they treat visible overlap as safe transfer by default. Building on an expert-validated corpus of 28,382 Bai-Chinese sentence pairs, an out-of-domain epigraphic set and a reproducible encoding pipeline, the dissertation will (1) diagnose semantic interference, (2) compare adaptation strategies under realistic compute constraints, and (3) estimate when shared-script transfer helps or harms adaptation. The long-term goal is Bai-capable understanding and generation. The dissertation addresses the prerequisite problem of safe and effective adaptation in a script-sharing low-resource setting."
}