Qais at BAREC Shared Task 2025: A Fine-Grained Approach for Arabic Readability Classification Using a pre-trained model
Year: 2025
Type: conference-paper
Abstract: In this paper, the results are presented within the context of the BAREC 2025 Shared Task (Elmadani et al., 2025a;Habash et al., 2025; Elmadani et al., 2025b) for Arabic text readability prediction.Participation in both the strict and open tracks achieved QWK scores of 82.5% and 83%, respectively.The proposed approach employs a 19-level fine-grained classification framework at the sentence level, leveraging the BAREC dataset (Elmadani et al., 2025a;Habash et al., 2025; Elmadani et al., 2025b) and transformer based AraBERT models.To address class imbalance, underrepresented levels were augmented with additional samples.By incorporating rich linguistic and structural features, including morphology, syntax, and vocabulary, the system surpasses less fine-grained methods in precision and reliability.
Author: Samar Ahmad
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037768