The aim of this article is to introduce Context-Free Grammars (CFG) for the Nawatl language. Nawatl is an Amerindian language of the 𝜋-language type, i.e. a language with few digital resources. For this reason the corpora available for the learning of Large Language Models (LLMs) are virtually non-existent, posing a significant challenge. The goal is to produce a substantial number of syntactically valid artificial Nawatl sentences and thereby to expand the corpora for the purpose of learning embeddings (static models or probably LLMs). For this objective, we introduce two new Nawatl CFGs and use them in generative mode. Thanks to these grammars, it is possible to expand Nawatl corpus significantly and subsequently to use it to learn embeddings (such as FastText) and to evaluate their relevance in semantic similarity tasks. The results show an improvement compared to the results obtained using only the original corpus without artificial expansion, and also demonstrate that economic embeddings often perform better than some LLMs.

In this paper, we aim to answer the following question: could corpus duplication be useful in Natural Language Processing (NLP) for low-resource languages? In these languages (or pi-languages), corpora available for training Large Language Models are virtually non-existent. Specifically, we study the impact of corpus expansion in Nahuatl, an agglutinative and polysynthetic Amerindian pi-language characterised by extensive dialectal variation. Our goal is to increase the size of Nahuatl corpora, which currently consist of a limited number of tokens, through controlled duplication techniques. Our experimental setup employs incremental duplication alongside appropriate corpus balancing, with the objective of training embeddings optimised for downstream NLP tasks. Consequently, static embeddings were trained and evaluated on a sentence-level semantic similarity task. Our results show a significant improvement in performance when incremental duplication is applied, compared to results obtained without corpus expansion. To our knowledge, this technique has not yet been explored in this field.

π-YALLI : a new corpus for Nahuatl Language Models The Nahuatl is a language with few computational resources, despite the fact that it is a living language spoken by around two million people. We built π-YALLI, a corpus that enables research and development of dynamic and static Language Models (LM). We measured the perplexity of π-YALLI, evaluating state-of-the-art LM performance on a manually annotated semantic similarity corpus relative to annotator agreement. The results show the difficulty of working with this π-language, but at the same time open up interesting perspectives for the study of other NLP tasks on Nahuatl.