@inproceedings{li-yang-2026-hllwan,
    title = "hllwan at {S}em{E}val-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis via {LLM} Feature Fusion and Test-Time Adaptation",
    author = "Li, Jinglong  and
      Yang, Yang",
    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://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.157/",
    pages = "1147--1152",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes the system developed bythe team for SemEval-2026 Task 3: Di-mensional Aspect-Based Sentiment Analysis(DimABSA). Unlike traditional categorical sen-timent analysis, predicting continuous Valenceand Arousal (VA) scores across multiple lan-guages and domains poses significant theoret-ical and engineering challenges. To systemat-ically address data scarcity and cross-domaindistribution shifts, we propose a highly robustframework. First, we implement a translation-based data augmentation strategy with preciseHTML-tag alignment to mitigate low-resourceconstraints. Second, we introduce an unsuper-vised opinion extraction module based on syn-tactic dependency parsing to explicitly capturesentiment-bearing words. Third, we designa Tripartite Feature Fusion architecture builtupon both encoder-only (DeBERTa-v3) andcausal LLM (Qwen2.5) models to dynamicallyaggregate global and localized aspect-opinionembeddings. Finally, we apply an unsupervisedTest-Time Adaptation (TTA) mechanism to cal-ibrate normalization layers on the fly. Our sys-tem demonstrates highly competitive perfor-mance while offering critical insights into thelimitations of LLMs in cross-lingual sentimenttransfer."
}