@inproceedings{le-phung-2026-alphalyrae,
    title = "{A}lpha{L}yrae at {S}em{E}val-2026 Task 9: Metric Learning and Asymmetric Loss for {C}hinese Polarization Analysis",
    author = "Le, Minh-Hoang  and
      Phung, Khoan",
    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-1.72/",
    pages = "503--508",
    ISBN = "979-8-89176-414-9",
    abstract = "For the Chinese track of SemEval-2026 Task 9 (Detecting Online Polarization), we address two key challenges: polarized content frequently uses implicit language (e.g., homophones and coded terms) to evade moderation, and class distributions exhibit severe long-tail imbalance. We propose a metric learning approach that frames polarization detection as semantic similarity matching, which captures implicit language patterns better than linear decision boundaries. We fine-tune an ERNIE-3.0 encoder with SoftTriple loss and apply ik/iNN retrieval for binary detection (Subtask 1). For multi-label categorization (Subtasks 2 and 3), we transfer learned representations from the detection model and fine-tune with Asymmetric Loss. A priority-based stratified cross-validation strategy ensures minority classes appear across all training folds despite extreme label skew. Evaluated on the official 1,927-sample test set using an end-to-end pipeline, our system achieved Macro-F1 scores of 0.9190 (Rank 6) on Polarization Detection, 0.8244 (Rank 5) on Type Classification, and 0.6670 (Rank 4) on Manifestation Identification."
}