Improve Arabic–English translation using Arabic Language Attention Transformer

Purpose The purpose of the proposed work is to enhance Arabic-English translation and other Arabic NLP tasks by addressing the unique linguistic challenges of the Arabic language. The Arabic Language Attention Transformer (ALAT) integrates morphological awareness, diacritic sensitivity, and right-to-left positional encoding to improve semantic understanding, syntactic accuracy, and contextual representation. By overcoming limitations in existing models like AraBERT and mBERT, ALAT aims to deliver superior performance in sentiment analysis, machine translation, and named entity recognition, setting a new benchmark for Arabic NLP applications. Design/methodology/approach The proposed ALAT employs an advanced attention-based architecture tailored for Arabic-English translation. It integrates a Morphological Awareness Module (MAM) to capture root-based patterns, a Diacritic-Aware Embedding Layer for semantic disambiguation, and a custom Right-to-Left Positional Encoding aligned with Arabic script. The model enhances the standard Transformer by incorporating morphological and diacritic features into its self-attention mechanism, improving contextual representation. ALAT is trained on diverse Arabic-English parallel corpora using cross-entropy loss with optimization techniques like the Adam optimizer, achieving superior performance in translation accuracy, as demonstrated by improved BLEU scores. Findings The proposed ALAT model significantly outperforms existing Arabic NLP models in key tasks. It achieved an F1-score of 92.4% in sentiment analysis, 86.1% in named entity recognition, and a BLEU score of 47.8 in machine translation, surpassing models like AraBERT and mBERT. ALAT's Morphological Awareness Module and Diacritic-Aware Embedding Layer improved semantic accuracy, while right-to-left positional encoding enhanced syntactic coherence. The model also demonstrated superior performance in morphological disambiguation (94.5% accuracy) and diacritic restoration (97.2% accuracy), establishing its effectiveness in addressing Arabic's linguistic complexities. Originality/value The originality of the proposed work lies in the development of the ALAT, a model specifically designed to address Arabic's unique linguistic challenges. Unlike existing models, ALAT integrates a Morphological Awareness Module, Diacritic-Aware Embedding Layer, and Right-to-Left Positional Encoding, enhancing semantic understanding and syntactic accuracy. Its innovative attention mechanism captures both morphological and diacritic features, significantly improving performance across NLP tasks. This work adds substantial value by setting a new standard for Arabic NLP, offering a robust, language-specific solution that outperforms general multilingual models in translation, sentiment analysis, and named entity recognition.