@inproceedings{de-gibert-etal-2023-four,
    title = "Four Approaches to Low-Resource Multilingual {NMT}: The {H}elsinki Submission to the {A}mericas{NLP} 2023 Shared Task",
    author = {De Gibert, Ona  and
      V{\'a}zquez, Ra{\'u}l  and
      Aulamo, Mikko  and
      Scherrer, Yves  and
      Virpioja, Sami  and
      Tiedemann, J{\"o}rg},
    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-1.20",
    doi = "10.18653/v1/2023.americasnlp-1.20",
    pages = "177--191",
    abstract = "The Helsinki-NLP team participated in the AmericasNLP 2023 Shared Task with 6 submissions for all 11 language pairs arising from 4 different multilingual systems. We provide a detailed look at the work that went into collecting and preprocessing the data that led to our submissions. We explore various setups for multilingual Neural Machine Translation (NMT), namely knowledge distillation and transfer learning, multilingual NMT including a high-resource language (English), language-specific fine-tuning, and multilingual NMT exclusively using low-resource data. Our multilingual Model B ranks first in 4 out of the 11 language pairs.",
}

The Helsinki-NLP team participated in the AmericasNLP 2023 Shared Task with 6 submissions for all 11 language pairs arising from 4 different multilingual systems. We provide a detailed look at the work that went into collecting and preprocessing the data that led to our submissions. We explore various setups for multilingual Neural Machine Translation (NMT), namely knowledge distillation and transfer learning, multilingual NMT including a high-resource language (English), language-specific fine-tuning, and multilingual NMT exclusively using low-resource data. Our multilingual Model B ranks first in 4 out of the 11 language pairs.
De Gibert, Ona V{\'a}zquez, Ra{\'u}l Aulamo, Mikko Scherrer, Yves Virpioja, Sami Tiedemann, J{\"o}rg
Four Approaches to Low-Resource Multilingual NMT: The Helsinki Submission to the AmericasNLP 2023 Shared Task