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In recent years, the widespread use of social media has led to an increase inthe generation of toxic and offensive content on online platforms. In response,social media platforms have worked on developing automatic detection methodsand employing human moderators to cope with this deluge of offensive content.While various state-of-the-art statistical models have been applied to detecttoxic posts, there are only a few studies that focus on detecting the words orexpressions that make a post offensive. This motivates the organization of theSemEval-2021 Task 5: Toxic Spans Detection competition, which has providedparticipants with a dataset containing toxic spans annotation in English posts.In this paper, we present the WLV-RIT entry for the SemEval-2021 Task 5. Ourbest performing neural transformer model achieves an $0.68$ F1-Score.Furthermore, we develop an open-source framework for multilingual detection ofoffensive spans, i.e., MUDES, based on neural transformers that detect toxicspans in texts.

WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans