Mexico is a country with a large number of indigenous languages, among which the most widely spoken is Nawatl, with more than two million people currently speaking it (mainly in North and Central America). Despite its rich cultural heritage, which dates back to the 15th century, Nawatl is a language with few computer resources. The problem is compounded when it comes to its dialectal varieties, with approximately 30 varieties recognised, not counting the different spellings in the written forms of the language. In this research work, we addressed the problem of classifying Nawatl varieties using Machine Learning and Neural Networks.
Cite
@article{arxiv.2601.02303,
title = {Classifying several dialectal Nawatl varieties},
author = {Juan-José Guzmán-Landa and Juan-Manuel Torres-Moreno and Miguel Figueroa-Saavedra and Carlos-Emiliano González-Gallardo and Graham Ranger and Martha Lorena-Avendaño-Garrido},
journal= {arXiv preprint arXiv:2601.02303},
year = {2026}
}