论文标题
SALAMNET在Semeval-2020 Task12：阿拉伯进攻性语言检测的深度学习方法
SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection
论文作者
论文摘要
本文描述了Salamnet是一种阿拉伯语进攻性语言检测系统，已提交给Semeval 2020共享任务12：社交媒体中的多语言进攻性语言识别。我们的方法着重于应用多个深度学习模型，并对结果进行深入误差分析，以为未来的开发考虑提供系统含义。为了实现我们的目标，已经开发了和评估了具有不同设计体系结构的复发性神经网络（RNN），一个封闭式的复发单元（GRU）和长期术语记忆（LSTM）模型。 SALAMNET是基于双向门的复发单元（BI-GRU）模型，报告的宏F1得分为0.83。
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.
