@inproceedings{iannielli-etal-2026-minds,
    title = "{MINDS} at {S}em{E}val-2026 Task 9: A Multi-Paradigm Approach to Cross-Lingual Polarization Detection",
    author = "Iannielli, Angelo  and
      Maroli, Samuele  and
      Roberto, Marco  and
      Sammartino, Stefano  and
      Vacirca, Valentino  and
      Savelli, Claudio  and
      Coppola, Riccardo  and
      Giobergia, Flavio",
    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-1.313/",
    pages = "2475--2486",
    ISBN = "979-8-89176-414-9",
    abstract = "Online polarization has become a central challenge in digital discourse, characterized by hostility, identity-based division, and culturally dependent expressions that vary across languages. Automatically detecting such phenomena is particularly difficult in multilingual settings, where semantic nuance and implicit rhetoric complicate cross-lingual generalization.In this context, we participate in POLAR, a shared task at SemEval 2026 on multilingual polarization detection and categorization across 22 languages. We compare three modeling paradigms: multilingual encoder fine-tuning, translation-based transfer learning, and prompting-based generative reasoning. For the multi-label categorization task, we introduce a two-stage cascaded architecture to mitigate false positives under severe class imbalance.Our results show that multilingual encoders achieve the most robust performance for binary detection, whereas reasoning-based prompting is competitive for fine-grained category classification. This comparative study highlights the strengths and limitations of each paradigm for cross-lingual polarization analysis."
}