@inproceedings{tonja-etal-2023-enhancing,
    title = "Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models",
    author = "Tonja, Atnafu Lambebo  and
      Nigatu, Hellina Hailu  and
      Kolesnikova, Olga  and
      Sidorov, Grigori  and
      Gelbukh, Alexander  and
      Kalita, Jugal",
    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://aclanthology.org/2023.americasnlp-1.22",
    doi = "10.18653/v1/2023.americasnlp-1.22",
    pages = "200--205",
    abstract = "This paper describes CIC NLP{'}s submission to the AmericasNLP 2023 Shared Task on machine translation systems for indigenous languages of the Americas. We present the system descriptions for three methods. We used two multilingual models, namely M2M-100 and mBART50, and one bilingual (one-to-one) {---} Helsinki NLP Spanish-English translation model, and experimented with different transfer learning setups. We experimented with 11 languages from America and report the setups we used as well as the results we achieved. Overall, the mBART setup was able to improve upon the baseline for three out of the eleven languages.",
}

This paper describes CIC NLP{'}s submission to the AmericasNLP 2023 Shared Task on machine translation systems for indigenous languages of the Americas. We present the system descriptions for three methods. We used two multilingual models, namely M2M-100 and mBART50, and one bilingual (one-to-one) {---} Helsinki NLP Spanish-English translation model, and experimented with different transfer learning setups. We experimented with 11 languages from America and report the setups we used as well as the results we achieved. Overall, the mBART setup was able to improve upon the baseline for three out of the eleven languages.
Tonja, Atnafu Lambebo Nigatu, Hellina Hailu Kolesnikova, Olga Sidorov, Grigori Gelbukh, Alex er Kalita, Jugal
Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models