Aspect-based sentiment analysis has evolved from categorical polarity classification to fine-grained modeling of continuous affective dimensions. Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends this paradigm by requiring both structured sentiment extraction and continuous valence–arousal (VA) regression in multilingual settings. In this paper, we present our system for SemEval-2026 Task 3, which evaluates this challenge across six languages and four domains, requiring systems to extract aspect–category–opinion quadruplets and predict VA scores on a 1–9 scale.We propose a modular four-stage multilingual transformer pipeline for element extraction, aspect–opinion pairing, category prediction, and VA regression. We conduct experiments over multiple models and training configurations, including VA rescaling to [-1,1], Gaussian label noise injection, Concordance Correlation Coefficient (CCC) loss, and Savitzky–Golay smoothing. Among all languages, our system achieves the lowest RMSE of 0.5333 on Subtask 1 and the highest cF1 of 0.5492 on Subtask 2. We further investigate data augmentation to improve low-resource performance and address label imbalance. Ultimately, our modular architecture demonstrated highly competitive cross-lingual transfer, achieving top-tier placements in low-resource settings, including 2nd place for Tatar and 6th place for Russian in dimensional regression.