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

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          Abstract

          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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          Author and article information

          Journal
          27 July 2020
          Article
          2007.13974
          07f55dbe-398f-44f6-a639-8f296ef5de17

          http://creativecommons.org/licenses/by/4.0/

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          Custom metadata
          Computation and Language (cs.CL)
          In Proceedings of the International Workshop on Semantic Evaluation (SemEval) 2020
          cs.CL cs.LG

          Theoretical computer science,Artificial intelligence
          Theoretical computer science, Artificial intelligence

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