Systematic Literature Review of Dialectal Arabic: Identification and Detection

It is becoming increasingly difficult to know who is working on what and how in computational studies of Dialectal Arabic. This study comes to chart the field by conducting a systematic literature review that is intended to give insight into the most and least popular research areas, dialects, machine learning approaches, neural network input features, data types, datasets, system evaluation criteria, publication venues, and publication trends. It is a review that is guided by the norms of systematic reviews. It has taken account of all the research that adopted a computational approach to dialectal Arabic identification and detection and that was published between 2000 and 2020. It collected, analyzed, and collated this research, discovered its trends, and identified research gaps. It revealed, inter alia, that our research effort has not been directed evenly between speech and text or between the vernaculars; there is some bias favoring text over speech, regional varieties over individual vernaculars, and Egyptian over all other vernaculars. Furthermore, there is a clear preference for shallow machine learning approaches, for the use of n-grams, TF-IDF, and MFCC as neural network features, and for accuracy as a statistical measure of validation of results. This paper also pointed to some glaring gaps in the research: (1) total neglect of Mauritanian and Bahraini in the continuous Arabic language area and of such enclave varieties as Anatolian Arabic, Khuzistan Arabic, Khurasan Arabic, Uzbekistan Arabic, the Subsaharan Arabic of Nigeria and Chad, Djibouti Arabic, Cypriot Arabic and Maltese; (2) scarcity of city dialect resources; (3) rarity of linguistic investigations that would complement our research; (4) and paucity of deep machine learning experimentation.