Simple But Not Naïve: Fine-Grained Arabic Dialect Identification Using Only N-Grams
Sohaila Eltanbouly, May Bashendy, Tamer Elsayed · 2019
This paper presents the participation of Qatar University team in MADAR shared task, which addresses the problem of sentence-level fine-grained Arabic Dialect Identification over 25 different Arabic dialects in addition to the Modern Standard Arabic.Arabic Dialect Identification is not a trivial task since different dialects share some features, e.g., utilizing the same character set and some vocabularies.We opted to adopt a very simple approach in terms of extracted features and classification models; we only utilize word and character ngrams as features, and Naïve Bayes models as classifiers.Surprisingly, the simple approach achieved non-naïve performance.The official results, reported on a held-out testing set, show that the dialect of a given sentence can be identified at an accuracy of 64.58% by our best submitted run.