@inproceedings{pashchenko-etal-2025-paragraph,
    title = "Paragraph-Level Machine Translation for Low-Resource {Finno-Ugric} Languages",
    author = "Pashchenko, Dmytro  and
      Yankovskaya, Lisa  and
      Fishel, Mark",
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.nodalida-1.50/",
    pages = "458--469",
    ISBN = "978-9908-53-109-0",
    abstract = "We develop paragraph-level machine translation for four low-resource Finno-Ugric languages: Proper Karelian, Livvi, Ludian, and Veps. The approach is based on sentence-level pre-trained translation models, which are fine-tuned with paragraph-parallel data. This allows the resulting model to develop a native ability to handle discource-level phenomena correctly, in particular translating from grammatically gender-neutral input in Finno-Ugric languages. We collect monolingual and parallel paragraph-level corpora for these languages. Our experiments show that paragraph-level translation models can translate sentences no worse than sentence-level systems, while handling discourse-level phenomena better. For evaluation, we manually translate part of FLORES-200 into these four languages. All our results, data, and models are released openly."
}