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Effective Multi Dialectal Arabic POS Tagging

Kareem Darwish
Mohammed Attia
Hamdy Mubarak
Younes Samih
Ahmed Abdelali
Lluís Màrquez
Mohamed Eldesouki
Laura Kallmeyer
Natural Language Engineering (NLE) (2020)


This work introduces robust multi-dialectal part of speech tagging trained on an annotated dataset of Arabic tweets in four major dialect groups: Egyptian, Levantine, Gulf, and Maghrebi. We implement two different sequence tagging approaches. The first uses Conditional Random Fields (CRF), while the second combines word and character-based representations in a Deep Neural Network with stacked layers of convolutional and recurrent networks with a CRF output layer. We successfully exploit a variety of features that help generalize our models, such as Brown clusters and stem templates. Also, we develop robust joint models that tag multi-dialectal tweets and outperform uni-dialectal taggers. We achieve a combined accuracy of 92.4% across all dialects, with per dialect results ranging between 90.2% and 95.4%. We obtained the results using a train/dev/test split of 70/10/20 for a dataset of 350 tweets per dialect.