Towards a Hybrid Rule-based and Statistical Arabic-French Machine Translation System

Arabic is a morphologically rich and complex language, which presents significant challenges for natural language processing and machine translation. In this paper, we describe an ongoing effort to build our first Arabic-French phrase– based machine translation system using the Moses decoder among other linguistic tools. The results show an improvement in the quality of translation and a gain in terms of Bleu score after introducing a pre-processing scheme for Arabic and applying some rules based on morphological variations of the source language. The proposed approach is completed without increasing the amount of training data or changing radically the algorithms that can affect the translation or training engines.