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Resources in high-resource languages have not been efficiently exploited inlow-resource languages to solve language-dependent research problems. Spanishand French are considered high resource languages in which an adequate level ofdata resources for informal online social behavior modeling, is observed.However, a machine translation system to access those data resources andtransfer their context and tone to a low-resource language like dialectalArabic, does not exist. In response, we propose a framework that localizescontents of high-resource languages to a low-resource language/dialects byutilizing AI power. To the best of our knowledge, we are the first work toprovide a parallel translation dataset from/to informal Spanish and Frenchto/from informal Arabic dialects. Using this, we aim to enrich theunder-resource-status dialectal Arabic and fast-track the research of diverseonline social behaviors within and across smart cities in differentgeo-regions. The experimental results have illustrated the capability of ourproposed solution in exploiting the resources between high and low resourcelanguages and dialects. Not only this, but it has also been proven thatignoring dialects within the same language could lead to misleading analysis ofonline social behavior.

Content-Localization based Neural Machine Translation for Informal Dialectal Arabic: Spanish/French to Levantine/Gulf Arabic