  The interest in statistical machine translation systems increases currently
due to political and social events in the world. A proposed Statistical Machine
Translation (SMT) based model that can be used to translate a sentence from the
source Language (English) to the target language (Arabic) automatically through
efficiently incorporating different statistical and Natural Language Processing
(NLP) models such as language model, alignment model, phrase based model,
reordering model, and translation model. These models are combined to enhance
the performance of statistical machine translation (SMT). Many implementation
tools have been used in this work such as Moses, Gizaa++, IRSTLM, KenLM, and
BLEU. Based on the implementation, evaluation of this model, and comparing the
generated translation with other implemented machine translation systems like
Google Translate, it was proved that this proposed model has enhanced the
results of the statistical machine translation, and forms a reliable and
efficient model in this field of research.
