We present a method for generating Colloquial Egyptian Arabic (CEA) from morphologically dis-ambiguated 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.
展开▼
