@article{guzman-landa-etal-2026-nawatl,
    title = "Nawatl Context-Free Grammars for Natural Language Processing",
    author = "Guzman Landa, Juan Jose  and
      Torres-Moreno, Juan-Manuel  and
      Ranger, Graham  and
      Figueroa-Saavedra, Miguel  and
      Quintana Torres, Ligia  and
      Gonzalez-Gallardo, Carlos-Emiliano  and
      Moreno Jimenez, Luis Gil  and
      Avenda{\~n}o Garrido, Martha Lorena",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.263/",
    pages = "3333--3342",
    abstract = "The aim of this article is to introduce Context-Free Grammars (CFG) for the Nawatl language. Nawatl is an Amerindian language of the $\pi$-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."
}