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