fBERT: A Neural Transformer for Identifying Offensive Content
Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe, Alexander G. Ororbia
Abstract
Transformer-based models such as BERT, XL-NET, and XLM-R have achieved state-of-theart 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.
Topics & Concepts
OffensiveTransformerComputer scienceArtificial intelligenceNatural language processingIdentification (biology)Language identificationLanguage modelSpeech recognitionMachine learningNatural languageEngineeringVoltageOperations researchElectrical engineeringBiologyBotanyHate Speech and Cyberbullying Detection