@inproceedings{christian-2026-creating,
    title = "Creating Grammar Teaching Material for Endangered Languages with Hybrid Grammar Induction",
    author = "Christian, Sebastien",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings 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-workshops/2026.findings-acl.1327/",
    pages = "26628--26643",
    ISBN = "979-8-89176-395-1",
    abstract = "Explicit grammar teaching is central to endangered-language revitalization, but creating grammar lessons is labor-intensive and often falls to already overburdened teachers. We present HYGRAM, a hybrid grammar-induction method that combines typological priors, Bayesian inference, constrained LLM reasoning, and retrieval from sparse corpora and descriptive documents to generate topic-specific grammar lessons for classroom use. HYGRAM targets extremely low-resource settings and can operate from a small elicited corpus collected in roughly 10 hours of fieldwork together with any available reference materials. We evaluate the system on six typologically diverse endangered languages using expert linguist judgments of output content quality, pedagogical adequacy, and consistency across generated lessons. Results indicate that HYGRAM can produce coherent and practically useful lessons, with better quality when modest explanatory evidence is available. Feedback from Pacific language communities further suggests relevance for ongoing revitalization efforts. Overall, the work shows that evidence-constrained hybrid grammar induction can support grammar teaching and documentation where standard NLP pipelines are infeasible."
}