@inproceedings{bokaei-etal-2026-smash,
    title = "{SMASH} at {S}em{E}val-2026 Task 9: Detecting Multilingual Polarisation with Encoder Ensembles and Calibrated Decision Thresholds",
    author = "Bokaei, Zahra  and
      Terranova, Alessandra  and
      Zheng, Yi  and
      Bidewell, Tom  and
      Ross, Bjorn",
    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-1.116/",
    pages = "832--848",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes the SMASH submission to SemEval-2026 Task{\textasciitilde}9 on multilingual, multicultural, and multi-event polarisation detection. The task comprises (i) binary polarisation detection, (ii) multi-label classification of polarisation types, and (iii) multi-label identification of polarisation manifestations across all available languages. We propose a language-adaptive ensemble framework combining monolingual and multilingual encoder-only transformers, together with a principled out-of-fold (OOF) threshold tuning strategy. Instead of relying on fixed probability thresholds, we jointly tune ensemble weights and class-wise decision thresholds to directly optimise macro-F1 under the official evaluation metric. Our experiments show that (1) monolingual encoders dominate in several high-resource languages but benefit from complementary multilingual signals, (2) no single multilingual backbone universally outperforms others across languages and subtasks, and (3) language-specific class threshold tuning substantially improves performance due to large cross-lingual variation in class distributions. Our results demonstrate that careful logit-level ensembling and threshold tuning provide strong performance for multilingual, imbalanced, multi-label polarisation detection. Across 22 evaluation languages, SMASH ranks among the top three systems in a substantial number of language{--}subtask pairs. Specifically, it ranks in the top three for 5 languages in Subtask 1, 14 languages in Subtask 2, and 16 languages in Subtask 3, demonstrating strong and consistent performance across diverse languages and tasks. Our system achieves average macro-F1 scores of 0.81, 0.62, and 0.53 for Subtasks 1, 2, and 3, respectively."
}