This paper presents the ArabicProcessors team’s system designed for sarcasm (subtask 1) and sentiment (subtask 2) detection shared task. We created a hybrid system by combining rule-based features and both static and dynamic embeddings using transformers and deep learning. The system’s architecture is an ensemble of Naive bayes, MarBERT and Mazajak embedding. This process scored an F1-score of 51% on sarcasm and 71% for sentiment detection.

This paper presents the ArabicProcessors team’s deep learning system designed for the NADI 2020 Subtask 1 (country-level dialect identification) and Subtask 2 (province-level dialect identification). We used Arabic-Bert in combination with data augmentation and ensembling methods. Unlabeled data provided by task organizers (10 Million tweets) was split into multiple subparts, to which we applied semi-supervised learning method, and finally ran a specific ensembling process on the resulting models. This system ranked 3rd in Subtask 1 with 23.26% F1-score and 2nd in Subtask 2 with 5.75% F1-score.