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

• URL: http://arxiv.org/abs/2110.03730v1
• Date: Thu, 7 Oct 2021 18:29:06 GMT
• Title: UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction
• Authors: Erik Yan and Harish Tayyar Madabushi
• Abstract summary: Toxicity is pervasive in social media and poses a major threat to the health of online communities. Recent introduction of pre-trained language models, which have achieved state-of-the-art results in many NLP tasks, has transformed the way in which we approach natural language processing.
• Score: 0.8376091455761259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Toxicity is pervasive in social media and poses a major threat to the health of online communities. The recent introduction of pre-trained language models, which have achieved state-of-the-art results in many NLP tasks, has transformed the way in which we approach natural language processing. However, the inherent nature of pre-training means that they are unlikely to capture task-specific statistical information or learn domain-specific knowledge. Additionally, most implementations of these models typically do not employ conditional random fields, a method for simultaneous token classification. We show that these modifications can improve model performance on the Toxic Spans Detection task at SemEval-2021 to achieve a score within 4 percentage points of the top performing team.

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