@inproceedings{vazquez-etal-2025-modular,
    title = "Modular Training of Deep Neural Networks for Text Classification in {G}uarani",
    author = "V{\'a}zquez Noguera, Jos{\'e} Luis  and
      Valdez, Carlos U.  and
      Mello-Rom{\'a}n, Julio C{\'e}sar  and
      Aguero, Marvin M.  and
      Colbes, Jos{\'e} D.  and
      Grillo, Sebasti{\'a}n A.",
    editor = "Estevanell-Valladares, Ernesto Luis  and
      Picazo-Izquierdo, Alicia  and
      Ranasinghe, Tharindu  and
      Mikaberidze, Besik  and
      Ostermann, Simon  and
      Gurgurov, Daniil  and
      Mueller, Philipp  and
      Borg, Claudia  and
      {\v{S}}imko, Mari{\'a}n",
    booktitle = "Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/old-master/2025.lowresnlp-1.8/",
    pages = "76--81",
    abstract = "We present a modular training approach for deep text classification in Guarani, where networks are split into sectors trained independently and later combined. This sector-wise backpropagation improves stability, reduces training time, and adapts to standard architectures like CNNs, LSTMs, and Transformers. Evaluated on three Guarani datasets{---}emotion, humor, and offensive language{---}our method outperforms traditional Bayesian-optimized training in both accuracy and efficiency."
}