@inproceedings{matkowska-etal-2026-team,
    title = "Team {JAT} at {S}em{E}val-2026 Task 9: Enhancing Polarization Detection with Cross-Lingual Transfer and Feature Fusion",
    author = "Matkowska, Aleksandra  and
      Lin, Taya  and
      Chao, Yu-Chun",
    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-1.302/",
    pages = "2402--2408",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe our system for SemEval-2026 Task9 (POLAR), Subtask 1 - binary polarizationdetection. Our approach investigates polariza-tion detection through monolingual and cross-lingual experimental settings. We first utilizea RoBERTa-based architecture enhanced withfeature fusion, combining contextual sentencerepresentations with handcrafted sentiment andintensity cues. As for multilingual joint train-ing, we explore it within the Indo-Europeanfamily to test whether cross-lingual transfer canelevate performance in data-scarce scenarios.Our final fine-tuned model achieves averageF1-score of 0.763 on the test set, compared to0.491 for a random baseline. We also reportablations for augmentation, feature fusion, andclass weighting to quantify each component{'}scontribution."
}