SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection

07/28/2020 ∙ by Fatemah Husain, et al. ∙ 0

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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meriem

Hi,

I came across your work on 'SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection' at catalyzex.com/paper/arxiv:2007.13974 and I found it really fascinating!

Could you please share the code with me so that I can better understand the implementation?

I promise to cite your work in any applications, papers, or articles.

 Thank you!

 

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