Deep learning approach for Translating Arabic Holy Quran into Italian language

Machine translation has become an urgent necessity due to the difference between the languages used within the world's societies. Automatic machine translation uses a computer to translate text or speech from one language to another. Arabic Language is one of the seven united nations officially spoken languages. Few efforts tried to translate officially Arabic language to other languages. The inaccurate Translation of Arabic language might lead to several problem especially when we translate the holy text of Quran into other language. The aim of this paper is to use neural machine translation to enhance the translation quality of Arabic to Italian. The main contribution of this paper is to propose two deep learning models, namely LSTM and GRU, with attention mechanism to translate the Arabic Holy Quran to Italian. The proposed models are evaluated based on the BLEU and ROUGE score. The experimental results indicate that the BLEU and ROUGE score of LSTM have achieved Average score 0.18 and 0.17 on BLEU and ROUGE score respectively. also, the result of GRU have achieved Average score 0.17 and 0.16 on BLEU score and ROUGE score respectively.