Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model
This work addresses the need for better offensive language detection in Arabic online communities, but it is incremental as it applies existing methods to new data with mixed results.
The study tackled the problem of detecting offensive language in Arabic online comments by fine-tuning BERT-based models on multiple datasets, finding that transfer learning had limited effects, especially for highly dialectal content.
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.