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

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arxiv 2007.13974 v1 pith:PJGRUQGJ submitted 2020-07-28 cs.CL cs.LG

Fatemah Husain , Jooyeon Lee , Samuel Henry , Ozlem Uzuner This is my paper · ORCID
classification cs.CLcs.LG
keywords languageoffensiverecurrentsalamnetapproacharabicbeendeep
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. Multi-task Learning with Active Learning for Arabic Offensive Speech Detection

    cs.CL 2025-06 conditional novelty 4.0 of 10

    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.

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