  Dialect and standard language identification are crucial tasks for many
Arabic natural language processing applications. In this paper, we present our
deep learning-based system, submitted to the second NADI shared task for
country-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-Task
Learning (MTL) model to tackle both country-level and province-level MSA/DA
identification. The latter MTL model consists of a shared Bidirectional Encoder
Representation Transformers (BERT) encoder, two task-specific attention layers,
and two classifiers. Our key idea is to leverage both the task-discriminative
and the inter-task shared features for country and province MSA/DA
identification. The obtained results show that our MTL model outperforms
single-task models on most subtasks.
