论文标题
阿拉伯进攻语言检测系统的微调方法：基于BERT的模型
Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model
论文作者
论文摘要
在线进攻语言的问题限制了在线用户的健康和安全性。必须在开发系统以检测在线进攻语言并确保在线社区的社会正义的系统中应用最新的最新技术。我们的研究调查了几个阿拉伯语进攻性语言数据集中微调的影响。我们开发了多个分类器，这些分类器单独使用四个数据集，并结合使用，以获取有关在线阿拉伯进攻内容的知识，并对用户进行相应的评论。我们的结果表明，转移学习对分类器绩效的影响有限，特别是对于高度方言评论。
The problem of online offensive language limits the health and security of online users. It is essential to apply the latest state-of-the-art techniques in developing a system to detect online offensive language and to ensure social justice to the online communities. Our study investigates the effects of fine-tuning across several Arabic offensive language datasets. We develop multiple classifiers that use four datasets individually and in combination in order to gain knowledge about online Arabic offensive content and classify users comments accordingly. Our results demonstrate the limited effects of transfer learning on the classifiers performance, particularly for highly dialectal comments.
