  In this paper, we tackle the Arabic Fine-Grained Hate Speech Detection shared
task and demonstrate significant improvements over reported baselines for its
three subtasks. The tasks are to predict if a tweet contains (1) Offensive
language; and whether it is considered (2) Hate Speech or not and if so, then
predict the (3) Fine-Grained Hate Speech label from one of six categories. Our
final solution is an ensemble of models that employs multitask learning and a
self-consistency correction method yielding 82.7% on the hate speech subtask --
reflecting a 3.4% relative improvement compared to previous work.
