@inproceedings{lam-ui-dhonnchadha-2026-grammar,
    title = "Grammar Engineering Meets {LLM}s: Development of {C}antonese and {I}rish {P}ar{G}ram Treebanks",
    author = "Lam, Chit-Fung  and
      U{\'i} Dhonnchadha, Elaine",
    editor = "Bernard, Timoth{\'e}e  and
      Chersoni, Emmanuele  and
      Rambelli, Giulia",
    booktitle = "Proceedings of the Third Workshop on the Bridges and Gaps between Formal and Computational Linguistics ({B}ri{G}ap-3)",
    month = jul,
    year = "2026",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-brigap/2026.brigap-1.11/",
    pages = "121--134",
    ISBN = "XXX-X-XXXXXX-XX-X",
    abstract = "Grammar engineering requires expertise in linguistic formalism and computational implementation, particularly in parallel grammar projects that balance cross-linguistic consistency with language-specific properties. This paper presents the development of Cantonese and Irish treebanks within the Parallel Grammar (ParGram) Project, where linguistic parallelism is maintained at an abstract functional level. We also investigated the methodological potential and limitations of using multilingual LLMs to support grammar engineering, focusing on Cantonese{--}Irish translation and the generation of formal syntactic structures using OpenAI{'}s gpt-oss-120b. The results showed that translation performance was generally unsatisfactory and unaffected by prompt language. For syntactic structure generation, the model produced some structurally meaningful outputs, but performed poorly on tasks requiring cross-linguistic abstraction. Nonetheless, LLM-generated outputs may still offer some reference value by suggesting alternative analyses and (partially) capturing predicate{--}argument relations. Overall, our findings highlight both the potential and limitations of using LLMs in collaborative grammar engineering, while underscoring the continued importance of expert-driven analysis and verification."
}