@inproceedings{sydorskyi-etal-2026-unlp,
    title = "The {UNLP} 2026 Shared Task on Multi-Domain Document Understanding",
    author = "Sydorskyi, Volodymyr  and
      Romanyshyn, Nataliia  and
      Kyslyi, Roman  and
      Nahorna, Olena",
    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/bulk-corrections-2026-07-02/2026.unlp-1.22/",
    pages = "249--259",
    ISBN = "979-8-89176-359-3",
    abstract = "This paper presents the results of the UNLP 2026 Shared Task on Multi-Domain Document Understanding. This Shared Task aims to challenge and assess AI capabilities to find the right information in a stack of domain-specific documents and generalize across domains. Participants were required not only to select the correct answer, but also to localize it by predicting the corresponding document and page. A total of 54 teams registered for the competition, 15 teams submitted systems, and 513 runs were evaluated on a hidden test set via Kaggle in a code-only submission format under constrained computational resources. The Kaggle leaderboard is left open for further submissions. Summarizing the contributions of this work, we establish a Ukrainian multi-domain document understanding benchmark, which consists of: (1) a collected dataset; (2) a proposed evaluation metric; and (3) an analysis of top-performing systems evaluated under a unified framework."
}