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Toxicity is pervasive in social media and poses a major threat to the healthof online communities. The recent introduction of pre-trained language models,which have achieved state-of-the-art results in many NLP tasks, has transformedthe way in which we approach natural language processing. However, the inherentnature of pre-training means that they are unlikely to capture task-specificstatistical information or learn domain-specific knowledge. Additionally, mostimplementations of these models typically do not employ conditional randomfields, a method for simultaneous token classification. We show that thesemodifications can improve model performance on the Toxic Spans Detection taskat SemEval-2021 to achieve a score within 4 percentage points of the topperforming team.

UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction