Deep Learning Algorithms for Arabic Machine Translation

Abstract

Arabic machine translation has advanced significantly with deep learning, despite the language’s complexity. Arabic’s rich morphology and variation between Modern Standard Arabic and dialects present major challenges. This paper reviews the evolution of machine translation from rule-based and statistical methods to neural models, focusing on Seq2Seq, attention mechanisms, and Transformers. It highlights the importance of multilingual and joint training to address data scarcity, especially for dialects. Key preprocessing steps such as normalization, tokenization, and morphological analysis are discussed for improving quality. The study also examines training strategies like pretraining and transfer learning, along with evaluation metrics such as BLEU and TER. It identifies challenges including limited resources and ambiguity, and explores applications, ethical issues, and future research directions.