@inproceedings{grandy-khir-2026-poldeck,
    title = "{P}ol{D}eck at {S}em{E}val-2026 Task 9: Multilingual Online Polarization Detection via Hybrid Model Ensembling and Data Augmentation",
    author = "Grandy, Ben  and
      Khir, Daniel",
    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-1.120/",
    pages = "879--885",
    ISBN = "979-8-89176-414-9",
    abstract = "In this paper, we address SemEval 2026 Task 9: Multilingual Online Polarization Detection. We present our hybrid ensemble framework, integrating few-shot prompting with Qwen3-30B, a native multilingual XLM-R encoder, and a translation-augmented DeBERTa encoder. To mitigate label imbalance, we implement a multi-stage augmentation pipeline leveraging LLMs for synthetic paraphrasing and cross-lingual translation. Our system ranked in the Top 10 on the English and German leaderboards, proving that integrating both high-capacity monolingual models and flexible multilingual models in a holistic system is a viable method for detecting online polarization. Our code is available on GitHub."
}