@inproceedings{gow-smith-snchez-villegas-2023-sheffields,
    title = "{S}heffield{'}s Submission to the {A}mericas{NLP} Shared Task on Machine Translation into Indigenous Languages",
    author = "Gow-Smith, Edward  and
      S{\'a}nchez Villegas, Danae",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Oncevay, Arturo  and
      Rice, Enora  and
      Rijhwani, Shruti  and
      Palmer, Alexis  and
      Kann, Katharina",
    booktitle = "Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.americasnlp-1.21/",
    doi = "10.18653/v1/2023.americasnlp-1.21",
    pages = "192--199",
    abstract = "The University of Sheffield took part in the shared task 2023 AmericasNLP for all eleven language pairs. Our models consist of training different variations of NLLB-200 model on data provided by the organizers and available data from various sources such as constitutions, handbooks and news articles. Our models outperform the baseline model on the development set on chrF with substantial improvements particularly for Aymara, Guarani and Quechua. On the test set, our best submission achieves the highest average chrF of all the submissions, we rank first in four of the eleven languages, and at least one of our models ranks in the top 3 for all languages."
}