@inproceedings{yang-etal-2025-towards,
    title = "What is it? Towards a Generalizable Native {A}merican Language Identification System",
    author = "Yang, Ivory  and
      Ma, Weicheng  and
      Alvarez, Carlos Guerrero  and
      Dinauer, William  and
      Vosoughi, Soroush",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-srw.10/",
    pages = "105--111",
    ISBN = "979-8-89176-192-6",
    abstract = "This paper presents a research thesis proposal to develop a generalizable Native American language identification system. Despite their cultural and historical significance, Native American languages remain entirely unsupported by major commercial language identification systems. This omission not only underscores the systemic neglect of endangered languages in technological development, but also highlights the urgent need for dedicated, community-driven solutions. We propose a two-pronged approach: (1) systematically curating linguistic resources across all Native American languages for robust training, and (2) tailored data augmentation to generate synthetic yet linguistically coherent training samples. As proof of concept, we extend an existing rudimentary Athabaskan language classifier by integrating Plains Apache, an extinct Southern Athabaskan language, as an additional language class. We also adapt a data generation framework for low-resource languages to create synthetic Plains Apache data, highlighting the potential of data augmentation. This proposal advocates for a community-driven, technological approach to supporting Native American languages."
}