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        <title>EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian</title>
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        <namePart type="given">Daryna</namePart>
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        <namePart type="given">Nikolay</namePart>
        <namePart type="family">Babakov</namePart>
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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            <namePart type="given">Tanmoy</namePart>
            <namePart type="family">Chakraborty</namePart>
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            <namePart type="given">Carolyn</namePart>
            <namePart type="family">Rose</namePart>
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    <abstract>While Ukrainian NLP has seen progress in many texts processing tasks, emotion classification remains an underexplored area with no publicly available benchmark to date. In this work, we introduce **EmoBench-UA**, the first annotated dataset for emotion detection in Ukrainian texts. Our annotation schema is adapted from the previous English-centric works on emotion detection (Mohammad et al., 2018; Mohammad, 2022) guidelines. The dataset was created through crowdsourcing using the Toloka.ai platform ensuring high-quality of the annotation process. Then, we evaluate a range of approaches on the collected dataset, starting from linguistic-based baselines, synthetic data translated from English, to large language models (LLMs). Our findings highlight the challenges of emotion classification in non-mainstream languages like Ukrainian and emphasize the need for further development of Ukrainian-specific models and training resources.</abstract>
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%0 Conference Proceedings
%T EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian
%A Dementieva, Daryna
%A Babakov, Nikolay
%A Fraser, Alexander
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F dementieva-etal-2025-emobench
%X While Ukrainian NLP has seen progress in many texts processing tasks, emotion classification remains an underexplored area with no publicly available benchmark to date. In this work, we introduce **EmoBench-UA**, the first annotated dataset for emotion detection in Ukrainian texts. Our annotation schema is adapted from the previous English-centric works on emotion detection (Mohammad et al., 2018; Mohammad, 2022) guidelines. The dataset was created through crowdsourcing using the Toloka.ai platform ensuring high-quality of the annotation process. Then, we evaluate a range of approaches on the collected dataset, starting from linguistic-based baselines, synthetic data translated from English, to large language models (LLMs). Our findings highlight the challenges of emotion classification in non-mainstream languages like Ukrainian and emphasize the need for further development of Ukrainian-specific models and training resources.
%U https://aclanthology.org/2025.findings-emnlp.107/
%P 2025-2048

Markdown (Informal)

[EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian](https://aclanthology.org/2025.findings-emnlp.107/) (Dementieva et al., Findings 2025)

• EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian (Dementieva et al., Findings 2025)

ACL

• Daryna Dementieva, Nikolay Babakov, and Alexander Fraser. 2025. EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 2025–2048, Suzhou, China. Association for Computational Linguistics.