Towards a model of statistical machine translation Arabic-French

The automatic translation of texts of human origin is a very complex application called to apprehend the universe open text, without any constraint on their nature or diversity. To solve this problem, several attempts have been initiated, each having the objective to obtain a better translation quality of the parallel corpora. But before the various ambiguities of the natural language, the problem of translation is far from easy to solve. To do this, and in order to increase the translation quality, we propose a model for the generation of different semantic cases relating to different components of the sentence to determine the meaning first and then generate the translation into the language target. This allowed us to obtain satisfactory results compared to similar studies using other techniques. In our approach, we used a model guided by the semantics to learn the techniques of translation with a similar human performance. Our project is to translate source sentences from Arabic to French through a statistical approach that includes a dictionary and that is automatically evaluated by the BLEU metric to ensure encouraging results even if the tools that we use are very limited.