Year: 2025
Type: conference-paper
Abstract: Navigating the complexities of Arabic readability prediction requires addressing the language's rich morphology and structural diversity.In the BAREC Shared Task 2025, we participated in all tracks using a stacked ensemble meta learning framework.Our approach combined seven fine-tuned transformer, whose outputs fed into a meta classifier trained on multiple features, including individual predictions, their average, and the average top prediction probabilities.On the blind test set, our ensemble achieved a Quadratic Weighted Kappa (QWK) of 86.4%, demonstrating the effectiveness of integrating diverse transformer encoders for fine grained Arabic readability classification and the potential of meta learning in morphologically rich contexts.
Authors: Mostafa Saeed, Rana Waly, Abdelaziz Ashraf Hussein
Institutions: New York University Abu Dhabi, Özyeğin University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: New York University Abu Dhabi
OpenAlex ID: W4416037800