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Dialect and standard language identification are crucial tasks for manyArabic natural language processing applications. In this paper, we present ourdeep learning-based system, submitted to the second NADI shared task forcountry-level and province-level identification of Modern Standard Arabic (MSA)and Dialectal Arabic (DA). The system is based on an end-to-end deep Multi-TaskLearning (MTL) model to tackle both country-level and province-level MSA/DAidentification. The latter MTL model consists of a shared Bidirectional EncoderRepresentation Transformers (BERT) encoder, two task-specific attention layers,and two classifiers. Our key idea is to leverage both the task-discriminativeand the inter-task shared features for country and province MSA/DAidentification. The obtained results show that our MTL model outperformssingle-task models on most subtasks.

BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification