Online English to Arabic Translation Enhancement by Domain Data Adaptation

the aim of this paper is to introduce a new technique that enhances online translation from English to Arabic for a specific domain. This enhancement is achieved by training a new "Arabic online engine translation" to "Arabic manual translation" model that corrects common errors in the online translation. This paper focuses on two popular online translation engines which are Google and Bing. The translation enhancement is done in two steps. First step is training the translation of each online engine on Moses toolkit separately to generate translation model that corrects the output of online translation engine, the result of this step is corrected Google translation and corrected Bing translation which is better than the original translation. Second step is choosing one sentence from the two corrected sentence using a language model trained on the manual Arabic translation which gives more enhancement than step 1.Two metrics are used to evaluate proposed model enhancement, BLEU Score (Bilingual Evaluation Understudy) and NIST Score (National Institute of Standards and Technology). The proposed model has obtained 32.44% BLEU Score over Google and 40.91% BLEU Score over Bing for LDC2004T14 dataset, 18.08% BLEU Score over Google and 26.55% BLEU Score over Bing for Distinct LDC2004T14 dataset, and 10.69% BLEU Score over Google and 9.94% BLEU Score over Bing for MEDAR dataset.