  Based on an annotated multimedia corpus, television series Mar{\=a}y{\=a}
2013, we dig into the question of ''automatic standardization'' of Arabic
dialects for machine translation. Here we distinguish between rule-based
machine translation and statistical machine translation. Machine translation
from Arabic most of the time takes standard or modern Arabic as the source
language and produces quite satisfactory translations thanks to the
availability of the translation memories necessary for training the models. The
case is different for the translation of Arabic dialects. The productions are
much less efficient. In our research we try to apply machine translation
methods to a dialect/standard (or modern) Arabic pair to automatically produce
a standard Arabic text from a dialect input, a process we call ''automatic
standardization''. we opt here for the application of ''statistical models''
because ''automatic standardization'' based on rules is more hard with the lack
of ''diglossic'' dictionaries on the one hand and the difficulty of creating
linguistic rules for each dialect on the other. Carrying out this research
could then lead to combining ''automatic standardization'' software and
automatic translation software so that we take the output of the first software
and introduce it as input into the second one to obtain at the end a quality
machine translation. This approach may also have educational applications such
as the development of applications to help understand different Arabic dialects
by transforming dialectal texts into standard Arabic.
