--- license: cc-by-sa-4.0 task_categories: - text-classification language: - ar tags: - readability size_categories: - 1K19):** The percentage of cases where reference and prediction classes match in the 19-level scheme. - **Accuracy (Acc7, Acc5, Acc3):** The percentage of cases where reference and prediction classes match after collapsing the 19 levels into 7, 5, or 3 levels, respectively. - **Adjacent Accuracy (±1 Acc19):** Also known as off-by-1 accuracy. The proportion of predictions that are either exactly correct or off by at most one level in the 19-level scheme. - **Average Distance (Dist):** Also known as Mean Absolute Error (MAE). Measures the average absolute difference between predicted and true labels. - **Quadratic Weighted Kappa (QWK):** An extension of Cohen’s Kappa that measures the agreement between predicted and true labels, applying a quadratic penalty to larger misclassifications (i.e., predictions farther from the true label are penalized more heavily). We provide evaluation scripts [here](https://github.com/CAMeL-Lab/barec-shared-task-2025). --- ## Citation If you use BAREC in your work, please cite the following papers: ``` @inproceedings{elmadani-etal-2025-readability, title = "A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment", author = "Elmadani, Khalid N. and Habash, Nizar and Taha-Thomure, Hanada", booktitle = "Findings of the Association for Computational Linguistics: ACL 2025", year = "2025", address = "Vienna, Austria", publisher = "Association for Computational Linguistics" } @inproceedings{habash-etal-2025-guidelines, title = "Guidelines for Fine-grained Sentence-level Arabic Readability Annotation", author = "Habash, Nizar and Taha-Thomure, Hanada and Elmadani, Khalid N. and Zeino, Zeina and Abushmaes, Abdallah", booktitle = "Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX)", year = "2025", address = "Vienna, Austria", publisher = "Association for Computational Linguistics" } ```