@inproceedings{saha-saha-2026-transformer,
    title = "transformer{\_}1376 at {S}em{E}val-2026 Task 9: A Multi-Stage Pipeline with Calibrated Ensembles and Lexical Post-Processing for Online Polarization Detection in {B}engali",
    author = "Saha, Shuvodwip  and
      Saha, Pritha",
    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-1.262/",
    pages = "2082--2088",
    ISBN = "979-8-89176-414-9",
    abstract = "The POLAR @ SemEval-2026 Task 9 deals with the detection of online polarization in a variety of multilingual and multicultural environments. Our team participated in Subtask 1 of the POLAR @ SemEval-2026 Task 9, which mainly deals with binary classification of textual sequences for the detection of polarized stances. In this paper, we proposed a strong classification system for Bengali language based on fine-tuning the BanglaBERT Large model. The methodology used here involves a stratified five-fold cross-validation approach along with a performance-weighted ensemble method, combined with temperature scaling probability calibration and a set of lexical post-processing rules."
}