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        <title>SokraTUM at SemEval-2026 Task 3: A hybrid cascade of Label Distribution Learning, RAG supported generative extraction and contrastive metric learning for dimensional sentiment analysis</title>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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            <namePart type="given">Kai</namePart>
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    <abstract>The Dimensional ABSA (DimABSA) sharedtask extends traditional aspect-based sentimentanalysis from categorical polarity to continuousvalence–arousal (VA) prediction. We presentour system for all three subtasks: DimensionalAspect Sentiment Regression (DimASR),Dimensional Aspect Sentiment Triplet Extrac-tion (DimASTE), and Dimensional AspectSentiment Quad Prediction (DimASQP).Due to the cascading nature of the differentsubtasks, we built a modular interlockingpipeline that uses classical Machine Learningand NLP methods.Experiments across domains show consistentgains in regression accuracy and structuredextraction performance. Our results demon-strate the effectiveness of distribution-awareregression, retrieval-augmented generation, andcontrastive prototype learning for dimensionalsentiment analysis.</abstract>
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%0 Conference Proceedings
%T SokraTUM at SemEval-2026 Task 3: A hybrid cascade of Label Distribution Learning, RAG supported generative extraction and contrastive metric learning for dimensional sentiment analysis
%A Laschenko, Denis
%A Korotyk, Albert
%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 laschenko-korotyk-2026-sokratum
%X The Dimensional ABSA (DimABSA) sharedtask extends traditional aspect-based sentimentanalysis from categorical polarity to continuousvalence–arousal (VA) prediction. We presentour system for all three subtasks: DimensionalAspect Sentiment Regression (DimASR),Dimensional Aspect Sentiment Triplet Extrac-tion (DimASTE), and Dimensional AspectSentiment Quad Prediction (DimASQP).Due to the cascading nature of the differentsubtasks, we built a modular interlockingpipeline that uses classical Machine Learningand NLP methods.Experiments across domains show consistentgains in regression accuracy and structuredextraction performance. Our results demon-strate the effectiveness of distribution-awareregression, retrieval-augmented generation, andcontrastive prototype learning for dimensionalsentiment analysis.
%U https://aclanthology.org/2026.semeval-1.241/
%P 1919-1929

Markdown (Informal)

[SokraTUM at SemEval-2026 Task 3: A hybrid cascade of Label Distribution Learning, RAG supported generative extraction and contrastive metric learning for dimensional sentiment analysis](https://aclanthology.org/2026.semeval-1.241/) (Laschenko & Korotyk, SemEval 2026)

• SokraTUM at SemEval-2026 Task 3: A hybrid cascade of Label Distribution Learning, RAG supported generative extraction and contrastive metric learning for dimensional sentiment analysis (Laschenko & Korotyk, SemEval 2026)

ACL

• Denis Laschenko and Albert Korotyk. 2026. SokraTUM at SemEval-2026 Task 3: A hybrid cascade of Label Distribution Learning, RAG supported generative extraction and contrastive metric learning for dimensional sentiment analysis. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 1919–1929, San Diego, California, USA. Association for Computational Linguistics.