  In this paper, we propose a neural machine translation system for Wolof, a
low-resource Niger-Congo language. First we gathered a parallel corpus of 70000
aligned French-Wolof sentences. Then we developped a baseline LSTM based
encoder-decoder architecture which was further extended to bidirectional LSTMs
with attention mechanisms. Our models are trained on a limited amount of
parallel French-Wolof data of approximately 35000 parallel sentences.
Experimental results on French-Wolof translation tasks show that our approach
produces promising translations in extremely low-resource conditions. The best
model was able to achieve a good performance of 47% BLEU score.
