@inproceedings{fetouh-etal-2026-reglat-semeval,
    title = "{REGLAT} at {S}em{E}val-2026 Task 9: Enhancing {A}rabic Online Polarization Detection Using {A}ra{BERT} and Synonym Replacement Augmentation",
    author = "Fetouh, Ahmed  and
      Francies, Mariam  and
      Ashraf, Nsrin  and
      Nayel, Hamada  and
      Mohammed, Rahmath",
    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-1.226/",
    pages = "1779--1783",
    ISBN = "979-8-89176-414-9",
    abstract = "In this paper, we present our system, which was submitted to SemEval-2026 Task 9 (Subtask 1: Polarization Detection) and focuses on binary classification of polarized content in Arabic social media text. To address Arabic linguistic variations, we propose a single-model approach that combines fine-tuned AraBERT with synonym-based data augmentation. On the Arabic bind set, our method achieves a competitive macro F1-score of 0.831 and an accuracy of 0.833. Among the 45 participating teams, our system ranked 11th overall, with a performance gap of 0.018 macro F1 from the top-ranked team (0.8488). The results show that a fine-tuned AraBERT with synonym replacement is a strong, simple, and reproducible baseline that outperforms more complex setups in dealing with Arabic attitude polarization nuances."
}