Several recent papers on Arabic dialect identification have hinted that using a word unigram model is sufficient and effective for the task. However, most previous work was done on a standard fairly homogeneous dataset of dialectal user comments. In this paper, we show that training on the standard dataset does not generalize, because a unigram model may be tuned to topics in the comments and does not capture the distinguishing features of dialects. We show that effective dialect identification requires that we account for the distinguishing lexical, morphological, and phonological phenomena of dialects. We show that accounting for such can improve dialect detection accuracy by nearly 10% absolute.
The approach used by REF for the identification of the Egyptian dialect was based on lexical, morphological and phonological information.
Verifiably Effective Arabic Dialect Identification
EMNLP
Computer Science