DUTH at SemEval-2026 Task 3: Multilingual Transformer Models for Dimensional Stance Prediction Across Tracks

AbstractThis 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.