Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 7 Feb 2022.
Builds on
6 of 23 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 359
- 2020KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Mediacited by 84
- 2018A Unified Deep Learning Architecture for Abuse Detectioncited by 37
- 2020OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approachcited by 11
- 2020Leveraging Affective Bidirectional Transformers for Offensive Language Detectioncited by 11
Built on by
Citing papers per year: 2022: 1.
1 paper.