@inproceedings{kapali-etal-2025-statistical,
    title = "Statistical and Neural Methods for {H}awaiian Orthography Modernization",
    author = "Kapali, Jaden  and
      Williamson, Keaton  and
      Wu, Winston",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.1782/",
    doi = "10.18653/v1/2025.emnlp-main.1782",
    pages = "35137--35143",
    ISBN = "979-8-89176-332-6",
    abstract = "Hawaiian orthography employs two distinct spelling systems, both of which are used by communities of speakers today. These two spelling systems are distinguished by the presence of the `okina letter and kahak{\={o}} diacritic, which represent glottal stops and long vowels, respectively. We develop several models ranging in complexity to convert between these two orthographies. Our results demonstrate that simple statistical n-gram models surprisingly outperform neural seq2seq models and LLMs, highlighting the potential for traditional machine learning approaches in a low-resource setting."
}