@inproceedings{guo-chang-2026-yeze,
    title = "{YEZE} at {S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling",
    author = "Guo, Fengze  and
      Chang, Yue",
    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-1.235/",
    pages = "1860--1873",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a multilingual system for SemEval-2026 Task 9 on detecting and characterizing online polarization across languages, cultures, and events. Our approach participates in all three subtasks and models each subtask independently using a heterogeneous weighted ensemble of XLM-RoBERTa-large and mDeBERTa-v3-base. For the multi-label settings, we adopt weighted binary cross-entropy to mitigate severe label imbalance. The system is trained exclusively on the provided task data and achieves robust performance across languages."
}