  Social media user-generated text is actually the main resource for many NLP
tasks. This text however, does not follow the standard rules of writing.
Moreover, the use of dialect such as Moroccan Arabic in written communications
increases further NLP tasks complexity. A dialect is a verbal language that
does not have a standard orthography, which leads users to improvise spelling
while writing. Thus, for the same word we can find multiple forms of
transliterations. Subsequently, it is mandatory to normalize these different
transliterations to one canonical word form. To reach this goal, we have
exploited the powerfulness of word embedding models generated with a corpus of
YouTube comments. Besides, using a Moroccan Arabic dialect dictionary that
provides the canonical forms, we have built a normalization dictionary that we
refer to as MANorm. We have conducted several experiments to demonstrate the
efficiency of MANorm, which have shown its usefulness in dialect normalization.
