@inproceedings{taien-hossen-2026-taien,
    title = "Taien at {S}em{E}val-2026 Task 9: Multilingual Polarization Detection Using Transformer-based Models",
    author = "Taien, Saida  and
      Hossen, Palash",
    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-1.260/",
    pages = "2070--2077",
    ISBN = "979-8-89176-414-9",
    abstract = "This submission describes a multilingual polarization detection system for SemEval-2026 Task 9. The system leverages parallel fine-tuning of XLM-RoBERTa and mDeBERTa-v3 transformer models with a probability-level ensemble to improve prediction reliability. We employ language-independent preprocessing, subword tokenization, and a standardized classification head for all 22 languages to ensure a consistent modeling framework across the multilingual setting. Experimental results demonstrate strong performance on both high-resource and low-resource languages, highlighting the effectiveness of the ensemble approach in stabilizing predictions and improving multilingual polarization detection."
}