Dialectal Arabic to English Machine Translation: Pivoting through Modern Standard Arabic

Wael Salloum and Nizar Habash

Modern Standard Arabic (MSA) has a wealth of natural language processing (NLP) tools and resources. In comparison, resources for dialectal Arabic (DA), the unstandardized spoken varieties of Arabic, are still lacking. We present ELISSA, a machine translation (MT) system for DA to MSA. ELISSA employs a rule-based approach that relies on morphological analysis, transfer rules and dictionaries in addition to language models to produce MSA paraphrases of DA sentences. ELISSA can be employed as a general preprocessor for DA when using MSA NLP tools. A manual error analysis of ELISSA’s output shows that it produces correct MSA translations over 93% of the time. Using ELISSA to produce MSA versions of DA sentences as part of an MSA-pivoting DA-to-English MT solution, improves BLEU scores on multiple blind test sets between 0.6% and 1.4%.