Neural Networks for Bilingual Machine Translation Model

Machine translation can be involved in statistical-based, corpus-based or dataset-based machine translation systems, in addition to linguistic systems. This paper objects to develop a bilingual English to Arabic translation model with quality for continuous improvement and flexible to be expanded multi-lingual other language pairs. This in addition to create an integrated translation environment that incorporates computer-assisted facilities to enhance the quality of automatically produced texts, increase translators' productivity and help their professional capabilities. Therefore, a machine translation model based on neural networks will be developed. Consequently, bilingual dictionaries will be involved, after cleaning and removing non-alphanumeric texts using linguistic modification tasks for the proposed machine translation model. Therefore, encoder and decoder models are involved for such machine translation. Finally, the training model is used to inference on new input to translate and therefore, the testing phase of the proposed machine translation model will be evaluated.