@inproceedings{stewart-kriukova-2026-morphological,
    title = "Morphological Parsing for {M}edia {L}engua: When Accessibility Matters More Than State-of-the-Art",
    author = "Stewart, Jesse  and
      Kriukova, Olga",
    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-1.1/",
    pages = "1--9",
    ISBN = "979-8-89176-422-4",
    abstract = "While machine learning approaches dominate contemporary NLP research, a critical gap exists between published models and tools actually used by target communities (Gessler {\&} von der Wense, 2024). This paper presents two morphological parsers for Media Lengua (ISO 639-3: mue), an endangered mixed language of Ecuador, demonstrating that a JavaScript rule-based system (98.6{\%} accuracy) can outperform a CRF model (95.7{\%} F1) while offering immediate community accessibility.Not all language structures permit straightforward rule-based parsing; however, when a language{'}s morphology allows for this approach with competitive accuracy, we argue that it should be preferred for its practical advantages: immediate browser-based deployment, transparency, zero infrastructure requirements, and long-term maintainability. Our rule-based parser runs entirely in the browser, is freely available online, and can be adapted to other Quechuan languages. In contrast, while the CRF model performs well on benchmarks, it requires additional infrastructure to become accessible.Our comparison highlights the need to evaluate NLP tools not only on accuracy metrics but also on accessibility and real-world adoption, which is particularly crucial for endangered language communities where sustainable, community-accessible tools can support language documentation, education, and revitalization."
}