Content-Localization based Neural Machine Translation for Informal
  Dialectal Arabic: Spanish/French to Levantine/Gulf Arabic
Fatimah Alzamzami Abdulmotaleb El Saddik
Resources in high-resource languages have not been efficiently exploited in
low-resource languages to solve language-dependent research problems. Spanish
and French are considered high resource languages in which an adequate level of
data resources for informal online social behavior modeling, is observed.
However, a machine translation system to access those data resources and
transfer their context and tone to a low-resource language like dialectal
Arabic, does not exist. In response, we propose a framework that localizes
contents of high-resource languages to a low-resource language/dialects by
utilizing AI power. To the best of our knowledge, we are the first work to
provide a parallel translation dataset from/to informal Spanish and French
to/from informal Arabic dialects. Using this, we aim to enrich the
under-resource-status dialectal Arabic and fast-track the research of diverse
online social behaviors within and across smart cities in different
geo-regions. The experimental results have illustrated the capability of our
proposed solution in exploiting the resources between high and low resource
languages and dialects. Not only this, but it has also been proven that
ignoring dialects within the same language could lead to misleading analysis of
online social behavior.