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The progress of Natural Language Processing (NLP), although fast in recentyears, is not at the same pace for all languages. African languages inparticular are still behind and lack automatic processing tools. Some of thesetools are very important for the development of these languages but also havean important role in many NLP applications. This is particularly the case forautomatic spell checkers. Several approaches have been studied to address thistask and the one modeling spelling correction as a translation task frommisspelled (noisy) text to well-spelled (correct) text shows promising results.However, this approach requires a parallel corpus of noisy data on the one handand correct data on the other hand, whereas Wolof is a low-resource languageand does not have such a corpus. In this paper, we present a way to address theconstraint related to the lack of data by generating synthetic data and wepresent sequence-to-sequence models using Deep Learning for spelling correctionin Wolof. We evaluated these models in three different scenarios depending onthe subwording method applied to the data and showed that the latter had asignificant impact on the performance of the models, which opens the way forfuture research in Wolof spelling correction.

Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector