@inproceedings{vaidya-2026-aatman,
    title = "Aatman at {S}em{E}val-2026 Task 9: Transfer Learning for Multilingual Polarization Detection",
    author = "Vaidya, Aatman",
    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-1.370/",
    pages = "2954--2959",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our system for Subtask 1 of SemEval-2026 Task 9: POLAR, which focuses on multilingual polarization detection. The task is formulated as a binary classification problem across 22 languages drawnfrom diverse online platforms and real-world events. We investigate three complementary approaches: supervised fine-tuning of multi-lingual encoder-only transformer models, zero-and few-shot classification using large language models (LLMs), and transfer learning from related harmful language tasks such as hate speech, toxicity, abusive language, and gender-based violence. Among the supervised models, mDeBERTa achieved the strongest baseline performance. Prompt-based methods with open-weight LLMs showed limited effectiveness, particularly in zero-shot settings. The best resultswere obtained using transfer learning, where the model was first fine-tuned on related task datasets and then adapted to the polarizationtask, achieving a Macro-F1 score of 0.81. Our findings indicate that supervised multilingualencoders remain highly effective for polarization detection and that incorporating related harmful language tasks can substantially improve performance, especially for nuanced and context-dependent expressions of polarization."
}