@inproceedings{brophy-wu-2026-low,
    title = "Low-Resource Methods for {H}awaiian Machine Translation",
    author = "Brophy, Nolan  and
      Wu, Winston",
    editor = "Agyapong, Godfred  and
      Moeller, Sarah  and
      Arppe, Antti  and
      Marashian, Ali  and
      Rosenblum, Daisy",
    booktitle = "Proceedings of the Ninth Workshop on the Use of Computational Methods in the Study of Endangered Languages ({C}omput{EL}-9)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.computel-1.11/",
    pages = "104--110",
    ISBN = "979-8-89176-422-4",
    abstract = "This paper investigates the challenges of low-resource machine translation for ʻ{\={O}}lelo Hawaiʻi (Hawaiian), a critically endangered Polynesian language. We compile a corpus of publicly available Hawaiian-English bitext and investigate the effectiveness of neural sequence-to-sequence models and large language models for translating Hawaiian. To address data scarcity, we employ various data augmentation techniques, including backtranslation, multilingual training using parallel corpora in related languages, and leveraging dictionary entries. Our experiments demonstrate that multilingual training significantly improves model performance, particularly when incorporating bitext from related Polynesian languages. Fine-tuned large language models were not able to outperform mBART, highlighting that smaller and simpler models are still relevant, especially in low-resource scenarios."
}