@inproceedings{gu-etal-2023-playground,
    title = "{P}lay{G}round Low Resource Machine Translation System for the 2023 {A}mericas{NLP} Shared Task",
    author = "Gu, Tianrui  and
      Chen, Kaie  and
      Ouyang, Siqi  and
      Li, Lei",
    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/jlcl-multiple-ingestion/2023.americasnlp-1.19/",
    doi = "10.18653/v1/2023.americasnlp-1.19",
    pages = "173--176",
    abstract = "This paper presents PlayGround`s submission to the AmericasNLP 2023 shared task on machine translation (MT) into indigenous languages. We finetuned NLLB-600M, a multilingual MT model pre-trained on Flores-200, on 10 low-resource language directions and examined the effectiveness of weight averaging and back translation. Our experiments showed that weight averaging, on average, led to a 0.0169 improvement in the ChrF++ score. Additionally, we found that back translation resulted in a 0.008 improvement in the ChrF++ score."
}