Classifying several dialectal Nawatl varieties

TL;DR

This paper explores classifying various Nawatl dialects using machine learning techniques to address the scarcity of computational resources for this indigenous language.

Contribution

It introduces a novel approach to classify Nawatl dialects employing machine learning and neural networks, filling a gap in computational resources for this language.

Findings

Successful classification of Nawatl dialects using ML models

Demonstrated effectiveness of neural networks for dialect identification

Provided a foundation for further computational linguistic research on Nawatl

Abstract

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.