@inproceedings{arampatzis-arampatzis-2026-duth-semeval,
    title = "{DUTH} at {S}em{E}val-2026 Task 3: Multilingual Transformer Models for Dimensional Stance Prediction Across Tracks",
    author = "Arampatzis, Georgios  and
      Arampatzis, Avi",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.85/",
    pages = "592--598",
    ISBN = "979-8-89176-414-9",
    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."
}