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.
@article{arxiv.2203.03542,
title = {Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model},
author = {Fatemah Husain and Ozlem Uzuner},
journal= {arXiv preprint arXiv:2203.03542},
year = {2022}
}
Comments
5 pages. Future Technologies and Innovations (FTI) Proceedings, 4th International Conference on Computer Applications and Information Security (ICCAIS 2021). arXiv admin note: substantial text overlap with arXiv:2102.05708