hdharpure at SemEval-2026 Task 3: BERT-Based Modeling and Prediction Behavior Analysis for Multilingual Valence–Arousal Scoring

AbstractThe SemEval-2026 Task 3 is a Dimensional aspect-based sentiment analysis (DimABSA) task which extends traditional ABSA by predicting continuous regression in two dimensions: valence (V) and arousal (A). The Track A/Subtask 1 represent multilingual task in which for a given text and aspects mentioned in it, there is a need to predict V/A scores for each aspect. Our approach is based on the pretraining-finetuning concept: we first pretrain multilingual model (M ′) followed by its fine-tuning (M ′′ l,d) on the training data of specific domain (d) and language (l). We adopt XLM-RoBERTa (M ) as the encoder with separate regression heads for valence and arousal prediction. Our experiments on manual split of official SemEval-2026 Task 3 dataset (D20% train) demonstrate that fine-tuning model in two stages (M ′′ l,d) results in average ≈ 1.36 times improvement by RMSEva over approach of direct fine-tuning (Ml,d). To investigate limitations of the existing approach we visualize and discuss limitations of our system. Our code is publicly available.