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

2007.13974Jul 28, 2020In Proceedings of the International Workshop on Semantic Evaluation (SemEval) 2020

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

Fatemah Husain, Jooyeon Lee, Samuel Henry, Ozlem Uzuner

Computation and Language Machine Learning

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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The paper addresses: SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection.
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.

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Author comment: In Proceedings of the International Workshop on Semantic Evaluation (SemEval) 2020