@inproceedings{alqifari-etal-2026-adabeval,
    title = "The {A}dab{E}val 2026 Shared Task on {A}rabic Politeness Detection",
    author = "Alqifari, Reem Fahad  and
      Al-Khalifa, Hend  and
      Ghezaiel, Nadia  and
      Bounnit, Maria  and
      Alhazmi, Hend Hamed  and
      Almasoud, Ameera Masoud  and
      Al-Ghamdi, Sharefah Ahmed  and
      Alfear, Noof Abdullah",
    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/paragraph-normalization/2026.osact-1.17/",
    doi = "10.63317/3sbqjqbib67c",
    pages = "151--156",
    abstract = "We present an overview of the AdabEval 2026 shared task, organized as part of the OSACT7 workshop (co-located with LREC 2026). This task introduces the first benchmark suite for politeness detection. It includes two subtasks: Politeness Classification (Subtask A) and Category Prediction (Subtask B). The task focuses on evaluating models' ability to recognize and categorize politeness phenomena in Arabic text. Evaluation was conducted using an automatic metric (macro F1-score). A total of 28 unique teams participated in the shared task. Of these, 13 teams submitted final system predictions across the two subtasks. The top-performing systems relied primarily on transformer-based architectures. The winning systems achieved macro F1-scores of 0.89 for Subtask A and 0.58 for Subtask B."
}