Mawdoo3 AI at MADAR Shared Task: Arabic Tweet Dialect Identification
Bashar Talafha, Wael Farhan, Ahmed Altakrouri, Hussein T. Al-Natsheh · 2019
Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one.In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user.We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features.Our results show that a simple LinearSVC can outperform any complex deep learning model given a set of curated features.With a relatively complex user voting mechanism, we were able to achieve a Macro-Averaged F1-score of 71.84% on MADAR shared subtask-2.Our best submitted model ranked second out of all participating teams.