@inproceedings{s-etal-2026-polarmind,
    title = "{P}olar{M}ind at {S}em{E}val-2026 Task 9: Leveraging {L}a{BSE} with Progressive Curriculum Learning for Multicultural Polarization",
    author = "s, Sandeep  and
      M, Mothish  and
      Sundar, Sachin",
    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-1.405/",
    pages = "3232--3239",
    ISBN = "979-8-89176-414-9",
    abstract = "Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural on texts where intergroup hostility is prevalent. The problem is particularly challenging due to the data scarcity for these tasks in the low-resource languages. Identifying such phenomena has become an activearea of research and is addressed in SemEval 2026 Task 9: Multilingual, Multicultural Online Polarization Detection. To address this problem we propose an architecture that leverages LaBSE embeddings{---}an unconventional choice typically reserved for retrieval tasks{---}toobtain strong cross-lingual learning which enhances scores in low-resource language by ascore up to 0.2 macro F1. Furthermore, we provide a comprehensive ablation study evaluatingthe performance of diverse encoder models in the Qwen model family within a retrieval-basedprompting framework."
}