@inproceedings{tran-2026-uit,
    title = "{UIT}-Polar at {S}em{E}val-2026 Task 9 Detecting Multilingual, Multicultural and Multievent Online Polarization",
    author = "Trần, Ho{\`a}n",
    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-1.19/",
    pages = "127--132",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a two-stage hybrid system forSemEval-2026 Task 9 on multilingual and mul-tievent online polarization detection. The firststage employs DeBERTa for high-recall binaryfiltering to mitigate severe class imbalance. Thesecond stage leverages Mistral for fine-grainedpolarization classification, enabling improvedsemantic reasoning over candidate instances.This coarse-to-fine design enhances robustnessand efficiency while preserving minority-classperformance. Our system achieves Top-5 results on the English test set, demonstratingthe effectiveness of integrating encoder-basedscreening with LLM-based refinement."
}