Transforming Standard Arabic to Colloquial Arabic

We present a method for generating Colloquial Egyptian Arabic (CEA) from morphologically disambiguated Modern Standard Arabic (MSA). When used in POS tagging, this process improves the accuracy from 73.24% to 86.84% on unseen CEA text, and reduces the percentage of out-of vocabulary words from 28.98% to 16.66%. The process holds promise for any NLP task targeting the dialectal varieties of Arabic; e.g., this approach may provide a cheap way to leverage MSA data and morphological resources to create resources for colloquial Arabic to English machine translation. It can also considerably speed up the annotation of Arabic dialects.