This paper describes CYUT’s system for SemEval-2026 Task~3 Track~B, a multilingual aspect-based dimensional sentiment regression task. We formulate the task as continuous Valence–Arousal (VA) prediction and adopt a multi-task learning (MTL) framework with auxiliary tasks automatically derived from gold VA annotations, including polarity, intensity, and quadrant classification. However, these coarse-grained labels may still suffer from regional imbalance in the VA space, leaving some regions with insufficient auxiliary supervision. To address this issue, we extend the system with Polar Multi-Zone Labeling (PMZL) and use its seven-zone variant, PMZL-7. PMZL-7 partitions the VA plane into one core neutral region and six non-central zones based on the directional distribution of non-central samples. This design reduces the risk of auxiliary-label imbalance while supplementing directional information that conventional auxiliary tasks cannot directly capture. We evaluate XLM-R and two generative pretrained models. Results show that PMZL-7 is strongly model-dependent: it provides more stable improvements for Qwen2 and Ministral, while its effect on XLM-R is less consistent. On the official test set, our system achieves the best performance on the NigerianPidgin subset among all participating systems.