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            <title>Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T The UNLP 2026 Shared Task on Multi-Domain Document Understanding
%A Sydorskyi, Volodymyr
%A Romanyshyn, Nataliia
%A Kyslyi, Roman
%A Nahorna, Olena
%Y Romanyshyn, Mariana
%S Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)
%D 2026
%8 May
%I Association for Computational Linguistics
%C Lviv, Ukraine
%@ 979-8-89176-359-3
%F sydorskyi-etal-2026-unlp
%X 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.
%U https://aclanthology.org/2026.unlp-1.22/
%P 249-259

Markdown (Informal)

[The UNLP 2026 Shared Task on Multi-Domain Document Understanding](https://aclanthology.org/2026.unlp-1.22/) (Sydorskyi et al., UNLP 2026)

• The UNLP 2026 Shared Task on Multi-Domain Document Understanding (Sydorskyi et al., UNLP 2026)

ACL

• Volodymyr Sydorskyi, Nataliia Romanyshyn, Roman Kyslyi, and Olena Nahorna. 2026. The UNLP 2026 Shared Task on Multi-Domain Document Understanding. In Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026), pages 249–259, Lviv, Ukraine. Association for Computational Linguistics.