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        <title>DUTH at SemEval-2026 Task 3: Multilingual Transformer Models for Dimensional Stance Prediction Across Tracks</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>This paper presents DUTH, our system forTrack A and Track B of SemEval-2026 Task 3on Dimensional Sentiment Analysis, focusing on the Dimensional Aspect-Based Sentiment Regression (DimASR) subtask. DimASRrequires predicting continuous Valence andArousal (VA) scores for aspect terms in opinionated text and stance targets in public-issuediscourse.Our approach uses a multilingual Transformerencoder fine-tuned end-to-end to jointly encodethe input text and its corresponding aspect orstance target, followed by a regression head forVAprediction. We evaluate DUTH on the official multilingual and multidomain datasets andcompare it against the shared-task baselines.Results show competitive performance, withimprovements over the strongest official baseline in Track A and over the mBERT baselinein Track B, while yielding consistently strongerpredictions for Valence than for Arousal.</abstract>
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%0 Conference Proceedings
%T DUTH at SemEval-2026 Task 3: Multilingual Transformer Models for Dimensional Stance Prediction Across Tracks
%A Arampatzis, Georgios
%A Arampatzis, Avi
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F arampatzis-arampatzis-2026-duth-semeval
%X This paper presents DUTH, our system forTrack A and Track B of SemEval-2026 Task 3on Dimensional Sentiment Analysis, focusing on the Dimensional Aspect-Based Sentiment Regression (DimASR) subtask. DimASRrequires predicting continuous Valence andArousal (VA) scores for aspect terms in opinionated text and stance targets in public-issuediscourse.Our approach uses a multilingual Transformerencoder fine-tuned end-to-end to jointly encodethe input text and its corresponding aspect orstance target, followed by a regression head forVAprediction. We evaluate DUTH on the official multilingual and multidomain datasets andcompare it against the shared-task baselines.Results show competitive performance, withimprovements over the strongest official baseline in Track A and over the mBERT baselinein Track B, while yielding consistently strongerpredictions for Valence than for Arousal.
%U https://aclanthology.org/2026.semeval-1.85/
%P 592-598

Markdown (Informal)

[DUTH at SemEval-2026 Task 3: Multilingual Transformer Models for Dimensional Stance Prediction Across Tracks](https://aclanthology.org/2026.semeval-1.85/) (Arampatzis & Arampatzis, SemEval 2026)

• DUTH at SemEval-2026 Task 3: Multilingual Transformer Models for Dimensional Stance Prediction Across Tracks (Arampatzis & Arampatzis, SemEval 2026)

ACL

• Georgios Arampatzis and Avi Arampatzis. 2026. DUTH at SemEval-2026 Task 3: Multilingual Transformer Models for Dimensional Stance Prediction Across Tracks. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 592–598, San Diego, California, USA. Association for Computational Linguistics.