@inproceedings{ulli-kumari-2026-polar,
    title = "{P}ol{AR} Bears at {S}em{E}val-2026 Task 9: Parameter-Efficient Fine-Tuning and Cross-Lingual Augmentation for Multilingual Polarization Detection",
    author = "Ulli, Vinay  and
      Kumari, Jyoti",
    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-1.279/",
    pages = "2209--2215",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our system for SemEval-2026 Task 9: Detecting Multilingual, Multicul-tural and Multievent Online Polarization. Wefocus on four low-resource Indian languages(Hindi, Bengali, Telugu, and Odia) across threesubtasks: Polarization Detection, Type Classi-fication, and Manifestation Identification. Toaddress data scarcity, we employ cross-lingualdata augmentation using IndicTrans2, expand-ing our dataset fourfold. Our unified architec-ture leverages Qwen3-4B-Instruct optimizedvia QLoRA, training a linear classification headon masked mean-pooled hidden states withonly {\ensuremath{\sim}}33M trainable parameters. Our systemachieved highly competitive results in Subtask1, with an average Macro F1 of 0.813 across alllanguages (peaking at 0.8668 for Telugu). Forthe complex multi-label frameworks of Sub-tasks 2 and 3, our results expose a significantpre-training bias within foundational LLMs;while Hindi maintained strong F1 scores of0.7008 and 0.7248, performance dropped con-siderably for the other three languages, high-lighting the ongoing challenges of cross-lingualtransfer for nuanced rhetorical techniques."
}