@inproceedings{korotkova-fishel-2024-estonian,
    title = "{E}stonian-Centric Machine Translation: Data, Models, and Challenges",
    author = "Korotkova, Elizaveta  and
      Fishel, Mark",
    editor = "Scarton, Carolina  and
      Prescott, Charlotte  and
      Bayliss, Chris  and
      Oakley, Chris  and
      Wright, Joanna  and
      Wrigley, Stuart  and
      Song, Xingyi  and
      Gow-Smith, Edward  and
      Bawden, Rachel  and
      S{\'a}nchez-Cartagena, V{\'i}ctor M  and
      Cadwell, Patrick  and
      Lapshinova-Koltunski, Ekaterina  and
      Cabarr{\~a}o, Vera  and
      Chatzitheodorou, Konstantinos  and
      Nurminen, Mary  and
      Kanojia, Diptesh  and
      Moniz, Helena",
    booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)",
    month = jun,
    year = "2024",
    address = "Sheffield, UK",
    publisher = "European Association for Machine Translation (EAMT)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.eamt-1.55/",
    pages = "647--660",
    abstract = "Machine translation (MT) research is most typically English-centric. In recent years, massively multilingual translation systems have also been increasingly popular. However, efforts purposefully focused on less-resourced languages are less widespread. In this paper, we focus on MT from and into the Estonian language. First, emphasizing the importance of data availability, we generate and publicly release a back-translation corpus of over 2 billion sentence pairs. Second, using these novel data, we create MT models covering 18 translation directions, all either from or into Estonian. We re-use the encoder of the NLLB multilingual model and train modular decoders separately for each language, surpassing the original NLLB quality. Our resulting MT models largely outperform other open-source MT systems, including previous Estonian-focused efforts, and are released as part of this submission."
}