# Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languages

arXiv: https://arxiv.org/abs/2501.15773 · HTML: https://mlsanity.com/paper/2501.15773/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus](https://mlsanity.com/paper/2010.14571/) (2020)
- [How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language](https://mlsanity.com/paper/2204.11909/) (2022)

## Built on by (most cited first, 3 of 3)

- [Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Planner](https://mlsanity.com/paper/2506.01301/) (2025)
- [Growing Through Experience: Scaling Episodic Grounding in Language Models](https://mlsanity.com/paper/2506.01312/) (2025)
- [Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language](https://mlsanity.com/paper/2505.18159/) (2025)

## Abstract (arXiv)

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.