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Natural Language Processing (NLP) research has made great advancements inrecent years with major breakthroughs that have established new benchmarks.However, these advances have mainly benefited a certain group of languagescommonly referred to as resource-rich such as English and French. Majority ofother languages with weaker resources are then left behind which is the casefor most African languages including Wolof. In this work, we present a parallelWolof/French corpus of 123,000 sentences on which we conducted experiments onmachine translation models based on Recurrent Neural Networks (RNN) indifferent data configurations. We noted performance gains with the modelstrained on subworded data as well as those trained on the French-Englishlanguage pair compared to those trained on the French-Wolof pair under the sameexperimental conditions.

Low-Resourced Machine Translation for Senegalese Wolof Language