@inproceedings{fesenko-etal-2026-mining,
    title = "Mining Native {U}krainian Paraphrases: A Multi-Source Comparison",
    author = "Fesenko, Vladyslav  and
      Dydyk-Meush, Hanna  and
      Mudryi, Volodymyr",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Fifth {U}krainian Natural Language Processing Conference ({UNLP} 2026)",
    month = may,
    year = "2026",
    address = "Lviv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2026-06/2026.unlp-1.17/",
    pages = "199--208",
    ISBN = "979-8-89176-359-3",
    abstract = "We introduce a Ukrainian paraphrase dataset mined from event-aligned news headlines and compare it with translated and LLM-generated data sources. Candidate pairs are retrieved from native Ukrainian news titles and filtered using semantic and lexical constraints to form a training corpus in a semi-automatic pipeline. Human evaluation indicates that the sources differ in useful ways: LLM-generated paraphrases are generally stronger in meaning preservation, whereas news-mined pairs offer greater lexical variation while remaining fluent and meaning-preserving. We tune mT5-large and mT0-large and evaluate them on several held-out test sets, including a human-validated subset. Relative to Spivavtor-large, the models achieve comparable semantic preservation with lower copying on the combined and human-validated sets. Overall, the findings highlight the value of naturally mined Ukrainian paraphrases as supervision for low-resource paraphrase generation."
}