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            <title>Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)</title>
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            <namePart type="given">Mariana</namePart>
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    <abstract>We introduce UAReviews, a multi-task Ukrainian-language dataset for emotion and intent classification comprising 11,580 annotated texts. The dataset combines two sources: citizen reviews of government digital services provided by the Ministry of Digital Transformation of Ukraine and Ukrainian-language Telegram posts drawn from the COSMUS corpus. Each text is annotated with both an emotion label following the Ekman taxonomy (seven classes) and an intent label (five classes), making it the first publicly available Ukrainian resource for joint emotion and intent analysis. Annotation was performed by students at the Anonymous Institution, with a gold standard subset (20\textbackslash%) validated by three independent annotators achieving Krippendorff’s alpha = 0.93. We establish baselines using single-task and multi-task fine-tuned XLM-RoBERTa models and analyze emotion to intent correlation. Both the dataset and the baseline models are publicly available.</abstract>
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%0 Conference Proceedings
%T UAReviews: A Multi-Task Ukrainian Dataset for Emotion and Intent Classification
%A Kyslyi, Roman
%A Pysmennyi, Ihor
%A Mykhailov, Denys
%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 kyslyi-etal-2026-uareviews
%X We introduce UAReviews, a multi-task Ukrainian-language dataset for emotion and intent classification comprising 11,580 annotated texts. The dataset combines two sources: citizen reviews of government digital services provided by the Ministry of Digital Transformation of Ukraine and Ukrainian-language Telegram posts drawn from the COSMUS corpus. Each text is annotated with both an emotion label following the Ekman taxonomy (seven classes) and an intent label (five classes), making it the first publicly available Ukrainian resource for joint emotion and intent analysis. Annotation was performed by students at the Anonymous Institution, with a gold standard subset (20\textbackslash%) validated by three independent annotators achieving Krippendorff’s alpha = 0.93. We establish baselines using single-task and multi-task fine-tuned XLM-RoBERTa models and analyze emotion to intent correlation. Both the dataset and the baseline models are publicly available.
%U https://aclanthology.org/2026.unlp-1.2/
%P 12-23

Markdown (Informal)

[UAReviews: A Multi-Task Ukrainian Dataset for Emotion and Intent Classification](https://aclanthology.org/2026.unlp-1.2/) (Kyslyi et al., UNLP 2026)

• UAReviews: A Multi-Task Ukrainian Dataset for Emotion and Intent Classification (Kyslyi et al., UNLP 2026)

ACL

• Roman Kyslyi, Ihor Pysmennyi, and Denys Mykhailov. 2026. UAReviews: A Multi-Task Ukrainian Dataset for Emotion and Intent Classification. In Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026), pages 12–23, Lviv, Ukraine. Association for Computational Linguistics.