AlexU-BackTranslation-TL at SemEval-2020 Task 12: Improving Offensive Language Detection Using Data Augmentation and Transfer Learning
Mai Ibrahim, Marwan Torki, Nagwa El-Makky · 2020
Social media platforms, online news commenting spaces, and many other public forums have become widely known for issues of abusive behavior such as cyber-bullying and personal attacks.In this paper, we use the annotated tweets of Offensive Language Identification Dataset (OLID) to train three levels of deep learning classifiers to solve the three sub-tasks associated with the dataset.Sub-task A is to determine if the tweet is toxic or not.Then, for offensive tweets, sub-task B requires determining whether the toxicity is targeted.Finally, for sub-task C, we predict the target of the offense; i.e. a group, individual or other entity.In our solution, we tackle the problem of class imbalance in the dataset by using back translation for data augmentation and utilizing fine-tuned BERT model in an ensemble of deep learning classifiers.We used this solution to participate in the three English sub-tasks of SemEval-2020 task 12.The proposed solution achieved 0.91393, 0.6300 and 0.57607 macro F1-average in sub-tasks A, B and C respectively.We achieved the 8th, 14th and 21st places for sub-tasks A, B and C respectively.