Holy Quran-Italian seq2seq Machine Translation with Attention Mechanism

Abstract

Many machine translation studies have used large parallel groups to address sets of major European dialects. However, due to the lack of sufficient parallel information, few studies have considered Italian and Arabic. Moreover, dictionary-based translations of the Holy Quran from Arabic to Italian are usually incorrect. The meaning of the Quran has not been translated correctly. Because the dictionary-based translation considers the Quran to be a traditional document and translates it in order. This paper contributes in two ways. First, it presents a parallel corpus of 6237 Italian-Arabic sentences. Second, the paper introduces two deep learning models namely, long-shortterm memory (LSTM) sequence-to-sequence with an attention mechanism and Gated Recurrent Units (GRU) sequence-to-sequence with an attention mechanism for Arabic to Italian machine translation. Each of the proposed models is evaluated based on BLEU, ROUGE, and Cosine Similarity scores. The results indicate that the LSTM-based neural machine translation (NMT) outperforms the GRU-based NMT framework. The experimental results indicate that the LSTM model achieved mean scores of 0.96, 0.91, and 0.90 for Cosine Similarity, BLEU, and ROUGE, respectively. The GRU model achieved average scores of 0.94, 0.89, and 0.88 for Cosine Similarity, BLEU, and ROUGE scores, respectively.