@inproceedings{guzman-landa-etal-2026-corpora,
    title = "Corpora duplication for {NLP} in low-resource languages: A case study of {N}ahuatl",
    author = "Guzman Landa, Juan Jose  and
      Torres-Moreno, Juan-Manuel  and
      Moreno Jimenez, Luis  and
      Linhares Pontes, Elvys  and
      Figueroa-Saavedra, Miguel  and
      Ranger, Graham  and
      Lorena Avenda{\~n}o Garrido, Martha",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Bui, Minh Duc  and
      Pugh, Robert  and
      Oncevay, Arturo  and
      Chiruzzo, Luis  and
      Solano, Rolando Coto  and
      Rijhwani, Shruti  and
      Von Der Wense, Katharina",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Indigenous Languages of the {A}mericas ({A}mericas{NLP})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.americasnlp-6.11/",
    pages = "115--127",
    ISBN = "979-8-89176-415-6",
    abstract = "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."
}