CYUT at SemEval-2026 Task 3: Multi-Task Dimensional Aspect Sentiment Regression with Polar Multi-Zone Labeling in VA Space
Year: 2026
Type: conference-paper
Abstract: 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 coarsegrained 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 Nigerian-Pidgin subset among all participating systems.
Authors: Shih-Hung Wu, Xian-Yan Chen, Yi-Min Jian
Institution: Chaoyang University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166883849