Leveraging Distance-Optimized Transformers for High-Performance Arabic Short Answers Grading

Abstract: This study presents comprehensive distance-optimized transformer architecture for Automated Arabic Short Answers Grading (AASAG) that systematically evaluates multiple semantic similarity measures. Short answer grading—assessment of responses typically 1-3 sentences long requiring conceptual understanding rather than factual recall—poses significant challenges in Arabic due to morphological complexity and limited computational resources. Our approach integrates pre-trained Arabic transformer models (AraBERT v02) with four distinct distance algorithms: cosine similarity, Manhattan distance, Euclidean distance, and dot-product calculations within a Siamese network architecture. Through systematic evaluation across three progressively enhanced datasets (original AR-ASAG, SemEvalaugmented, and reference-integrated versions), our distance-optimized approach achieves state-of-the-art performance with correlation coefficients of 0.7998, representing a 5.5% improvement over existing methods. This advancement significantly outperforms traditional vector space models (0.7037 correlation), BERT-based approaches (0.7616), and hybrid semantic analysis methods (0.745), establishing new benchmarks for Arabic educational assessment technology.

Hatem M. Noaman, Mohsen Rashwan and Hazem Raafat. “Leveraging Distance-Optimized Transformers for High-Performance Arabic Short Answers Grading”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.9 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160980

@article{Noaman2025,
title = {Leveraging Distance-Optimized Transformers for High-Performance Arabic Short Answers Grading},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160980},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160980},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {9},
author = {Hatem M. Noaman and Mohsen Rashwan and Hazem Raafat}
}