YangSteam at SemEval-2026 Task 3: Transformer-Based Aspect-Aware Regression for Dimensional Sentiment and Stance Analysis
Year: 2026
Type: conference-paper
Abstract: This paper describes our system for the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA).We participate in Track A (DimABSA) and Track B (DimStance), both of which involve Subtask 1 -predicting continuous valencearousal (VA) scores for given text-aspect pairs in English and Chinese.Our system combines pre-trained multilingual transformers with aspect-marker input encoding and dual regression heads for VA prediction, trained with a 5-fold cross-validation ensemble.We select XLM-RoBERTa-large as the backbone for Track A and mDeBERTa-v3-base for Track B based on systematic model comparison on the development sets.On the official test sets, our system substantially outperforms the organizerprovided baselines across all language domain settings.On the unofficial post-evaluation leaderboard, the system achieves strong results on Chinese subsets, ranking 1st on zho-env (Track B) and 2nd on zho-fin (Track A).
Authors: Tsung-Hsien Yang, Shu-Fei Yang
Institution: Chunghwa Telecom (Taiwan)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166836712