2025 Asian Conference on Communication and Networks (ASIANComNet 2025)

The Arabic language presents unique challenges for natural language processing due to its complex grammar, diverse dialects, and frequent omission of diacritics. This paper proposes a unified token-free model based on ByT5 that simultaneously performs spelling correction (including Jordanian dialect-to-Modern Standard Arabic (MSA) translation) and diacritization. Our approach uses task-specific prefixes (“correct:” for correction and “diacritize:” for combined correction and diacritization) to enable flexible multi-task learning. The model was fine-tuned on the JODA dataset (Jordanian dialect/MSA pairs) and high-quality Tashkeela subsets (Clean-50 and Clean-400), with synthetic errors injection to enhance robustness. Automatic evaluation showed an overall evaluation score of 78.06% on JODA and 92.45% on the combined test set of JODA and Tashkeela. Manual evaluation of 200 JODA samples revealed a character error rate of 4.41% and diacritic error rate of 1.32%, demonstrating practical efficacy in handling Arabic’s complexities.

Arabic NLP,Dialect Translation,Jordanian Dialect,Diacritization,Spelling Correction,ByT5,Transformer Models,Multi-Task Learning