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

arXiv: https://arxiv.org/abs/2007.13974 · HTML: https://mlsanity.com/paper/2007.13974/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets](https://mlsanity.com/paper/2407.02448/) (2024)
- [A Group-Specific Approach to NLP for Hate Speech Detection](https://mlsanity.com/paper/2304.11223/) (2023)
- [Multi-task Learning with Active Learning for Arabic Offensive Speech Detection](https://mlsanity.com/paper/2506.02753/) (2025)

## Abstract (arXiv)

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.