Translating between morphologically rich languages is still challenging for actual machine translation systems. In this paper, we experiment with various Neural Machine Translation (NMT) architectures to address the data sparsity problem caused by data availability (quantity), domain shift and the languages involved (Arabic and French). We showed that the Factored NMT (FNMT) model, which uses linguistically motivated factors, is able to outperform standard NMT systems using subword units by more than 1% BLEU point even when a large quantity of data is available. Our work shows the benefits of applying linguistic factors in NMT when faced with low and large resource conditions.