@inproceedings{wu-etal-2026-cyut,
title = "{CYUT} at {S}em{E}val-2026 Task 3: Multi-Task Dimensional Aspect Sentiment Regression with Polar Multi-Zone Labeling in {VA} Space",
author = "Wu, Shih-Hung and
Chen, Xian-Yan and
Jian, Yi-Min",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.23/",
pages = "153--159",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes CYUT{'}s system for SemEval-2026 Task{\textasciitilde}3 Track{\textasciitilde}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."
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<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 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.</abstract>
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%0 Conference Proceedings
%T CYUT at SemEval-2026 Task 3: Multi-Task Dimensional Aspect Sentiment Regression with Polar Multi-Zone Labeling in VA Space
%A Wu, Shih-Hung
%A Chen, Xian-Yan
%A Jian, Yi-Min
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F wu-etal-2026-cyut
%X 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.
%U https://aclanthology.org/2026.semeval-1.23/
%P 153-159
Markdown (Informal)
[CYUT at SemEval-2026 Task 3: Multi-Task Dimensional Aspect Sentiment Regression with Polar Multi-Zone Labeling in VA Space](https://aclanthology.org/2026.semeval-1.23/) (Wu et al., SemEval 2026)
ACL