Offensive Language Detection in Arabic using ULMFiT

Mohamed Abdellatif, Ahmed Elgammal

Abstract:

In this paper, we approach the shared task OffenseEval 2020 by Mubarak et al. (2020) using ULMFiT Howard and Ruder (2018)
pre-trained on Arabic Wikipedia Khooli (2019) which we use as a starting point and use the target data-set to fine-tune it. The data set of
the task is highly imbalanced. We train forward and backward models and ensemble the results. We report confusion matrix, accuracy,
precision, recall and F1 of the development set and report summarized results of the test set. Transfer learning method using ULMFiT
shows potential for Arabic text classification.