SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection
This work addresses the need for automated offensive language detection in Arabic social media, but it is incremental as it applies standard deep learning models to a specific dataset.
The paper tackled the problem of detecting offensive language in Arabic social media by developing SalamNET, a deep learning system based on a Bi-directional Gated Recurrent Unit (Bi-GRU) model, which achieved a macro-F1 score of 0.83 in the SemEval-2020 Task12 evaluation.
This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83.