@inproceedings{lee-2026-joshualee2,
    title = "Joshualee2 at {S}em{E}val-2026 Task 9: Cross-Lingual Transformer-Based Polarization Detection",
    author = "Lee, Joshua",
    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-1.347/",
    pages = "2760--2764",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our system for POLAR Subtask 1 on multilingual polarization detection. The task involves binary sequence classification over 22 languages, where the model aims to predict whether a given text exhibits polarized discourse. To deal with the multilingual and resource-imbalanced nature of the dataset, we fine-tune the XLM-R, a pre-trained multilingual transformer encoder, using a language-aware sampling strategy that combines all available training data into a unified multilingual corpus. Our system achieves an overall macro-F1 of 0.781 and an average accuracy of 0.823 on the official test set. Results show strong performance in low-resource languages, though some discrepancies indicate remaining class imbalance."
}