@inproceedings{zwawi-wu-2026-shu,
    title = "{SHU} at {A}dab{E}val 2026: Category-Aware Fine-Tuning of {MARBERT} for {A}rabic Politeness and Pragmatic Function Classification",
    author = "Zwawi, Alla  and
      Wu, Stephen",
    editor = "Al-Khalifa, Hend  and
      El-Haj, Mo  and
      Ezzini, Saad",
    booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/revision-workflow/2026.osact-1.20/",
    doi = "10.63317/5csq27bytjtc",
    pages = "174--178",
    abstract = "This paper describes our submission to the AdabEval 2026 shared task, addressing Subtask A (politeness classification) and Subtask B (multi-label pragmatic category prediction). For Subtask A, we fine-tuned MARBERT using weighted cross-entropy to mitigate class imbalance. For Subtask B, we apply BCEWithLogitsloss with inverse-frequency positive weighting to address the minority categories, and we introduce a category merging strategy to reduce categories' sparsity and annotation variation. Finally, we propose a stacked architecture where predicted pragmatic categories are injected as auxiliary features into the politeness classifier. Our results demonstrate that dialect-aware modelling, class-imbalance handling, and category-aware stacking improve Macro-F1 across both subtasks, achieving 0.85 for Subtask A and 0.55 for Subtask B on the test set."
}