Mawdoo3 ai at madar shared task: Arabic fine-grained dialect identification with ensemble learning

Abstract

In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1). We propose an ensemble model of a group of experimentally designed best performing classifiers on a various set of features. Our system achieves an accuracy of 69:3% macro F1-score with an improvement of 1:4% accuracy from the baseline model on the DEV dataset. Our best run submitted model ranked as third out of 19 participating teams on the TEST dataset with only 0:12% macro F1-score behind the top ranked system.