  Social media are pervasive in our life, making it necessary to ensure safe
online experiences by detecting and removing offensive and hate speech. In this
work, we report our submission to the Offensive Language and hate-speech
Detection shared task organized with the 4th Workshop on Open-Source Arabic
Corpora and Processing Tools Arabic (OSACT4). We focus on developing purely
deep learning systems, without a need for feature engineering. For that
purpose, we develop an effective method for automatic data augmentation and
show the utility of training both offensive and hate speech models off (i.e.,
by fine-tuning) previously trained affective models (i.e., sentiment and
emotion). Our best models are significantly better than a vanilla BERT model,
with 89.60% acc (82.31% macro F1) for hate speech and 95.20% acc (70.51% macro
F1) on official TEST data.
