FBERT: A Neural Transformer for Identifying Offensive Content
This work addresses the important issue of offensive language detection for social media platforms, but it is incremental as it applies an existing method to new data.
The paper tackled the problem of identifying offensive content in social media by retraining a BERT model on the largest English offensive language corpus, achieving state-of-the-art performance across multiple datasets.
Transformer-based models such as BERT, XLNET, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the identification of offensive language and hate speech, an important problem in social media. In this paper, we present fBERT, a BERT model retrained on SOLID, the largest English offensive language identification corpus available with over $1.4$ million offensive instances. We evaluate fBERT's performance on identifying offensive content on multiple English datasets and we test several thresholds for selecting instances from SOLID. The fBERT model will be made freely available to the community.