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            <namePart type="given">Richard</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Paragraph-Level Machine Translation for Low-Resource Finno-Ugric Languages
%A Pashchenko, Dmytro
%A Yankovskaya, Lisa
%A Fishel, Mark
%Y Johansson, Richard
%Y Stymne, Sara
%S Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)
%D 2025
%8 March
%I University of Tartu Library
%C Tallinn, Estonia
%@ 978-9908-53-109-0
%F pashchenko-etal-2025-paragraph
%X 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.
%U https://aclanthology.org/2025.nodalida-1.50/
%P 458-469

Markdown (Informal)

[Paragraph-Level Machine Translation for Low-Resource Finno-Ugric Languages](https://aclanthology.org/2025.nodalida-1.50/) (Pashchenko et al., NoDaLiDa 2025)

• Paragraph-Level Machine Translation for Low-Resource Finno-Ugric Languages (Pashchenko et al., NoDaLiDa 2025)

ACL

• Dmytro Pashchenko, Lisa Yankovskaya, and Mark Fishel. 2025. Paragraph-Level Machine Translation for Low-Resource Finno-Ugric Languages. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 458–469, Tallinn, Estonia. University of Tartu Library.