Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languages

TL;DR

This paper presents a highly accurate random forest classifier for Navajo language detection, addressing the lack of native support in existing tools and demonstrating potential for broader application to related Athabaskan languages.

Contribution

Introduces a simple yet effective classifier trained on Navajo and similar languages, improving language detection for endangered Athabaskan languages and highlighting the need for culturally inclusive NLP tools.

Findings

Achieves 97-100% accuracy in Navajo detection

Demonstrates robustness across related Athabaskan languages

Highlights the importance of linguistic diversity in NLP

Abstract

Endangered languages, such as Navajo - the most widely spoken Native American language - are significantly underrepresented in contemporary language technologies, exacerbating the challenges of their preservation and revitalization. This study evaluates Google's Language Identification (LangID) tool, which does not currently support any Native American languages. To address this, we introduce a random forest classifier trained on Navajo and twenty erroneously suggested languages by LangID. Despite its simplicity, the classifier achieves near-perfect accuracy (97-100%). Additionally, the model demonstrates robustness across other Athabaskan languages - a family of Native American languages spoken primarily in Alaska, the Pacific Northwest, and parts of the Southwestern United States - suggesting its potential for broader application. Our findings underscore the pressing need for NLP systems that prioritize linguistic diversity and adaptability over centralized, one-size-fits-all solutions, especially in supporting underrepresented languages in a multicultural world. This work directly contributes to ongoing efforts to address cultural biases in language models and advocates for the development of culturally localized NLP tools that serve diverse linguistic communities.