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

Tharindu Ranasinghe, Diptanu Sarkar, Marcos Zampieri, Alexander Ororbia

    Research output: Chapter in Book/Published conference outputConference publication


    In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content. While various state-of-the-art statistical models have been applied to detect toxic posts, there are only a few studies that focus on detecting the words or expressions that make a post offensive. This motivates the organization of the SemEval-2021 Task 5: Toxic Spans Detection competition, which has provided participants 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. Our best performing neural transformer model achieves an 0.68 F1-Score. Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts.
    Original languageEnglish
    Title of host publicationThe 15th International Workshop on Semantic Evaluation (SemEval-2021)
    PublisherAssociation for Computational Linguistics (ACL)
    Number of pages8
    Publication statusPublished - Aug 2021
    Event15th International Workshop on Semantic Evaluation (SemEval-2021) - Online
    Duration: 5 Aug 20226 Aug 2022


    Conference15th International Workshop on Semantic Evaluation (SemEval-2021)
    Abbreviated titleSemEval
    Internet address

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