Phantoms at BAREC Shared Task 2025: Enhancing Arabic Readability Prediction with Hybrid BERT and Linguistic Features

Résumé

This paper describes our system for the BAREC 2025 Shared Task on Arabic Readability Assessment.Our approach is centered on a hybrid model that combines the deep contextual representations of a pre-trained transformer (AraBERTv02) with a rich set of engineered linguistic features.We extracted over 200 lexical, morphological, syntactic, and semantic features, which were refined to the 100 most informative ones through a multi-stage selection process.Our final model demonstrates significant effectiveness, achieving a Quadratic Weighted Kappa (QWK) of 82.7% and an exact accuracy of 57.6% on the official blind test set.These results highlight the powerful synergy between transformer-based embeddings and explicit linguistic signals for the nuanced task of assessing Arabic text readability.

Citer ce document

Accès au document

Texte intégral en lecture en ligne, réservé aux abonnés SPHAERO et aux membres de l'institution. Se connecter

Auteur(s)

Statistiques

Consultations : 1

Téléchargements : 0