A multi-task Arabic offensive speech detector with entropy-based active learning and weighted emoji tokens reports 85.42% macro F1 on OSACT2022 using roughly 3,300 training samples.
SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection
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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.
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Multi-task Learning with Active Learning for Arabic Offensive Speech Detection
A multi-task Arabic offensive speech detector with entropy-based active learning and weighted emoji tokens reports 85.42% macro F1 on OSACT2022 using roughly 3,300 training samples.