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Detecting which parts of a sentence contribute to that sentence's toxicity --rather than providing a sentence-level verdict of hatefulness -- would increasethe interpretability of models and allow human moderators to better understandthe outputs of the system. This paper presents our team's, UTNLP, methodologyand results in the SemEval-2021 shared task 5 on toxic spans detection. We testmultiple models and contextual embeddings and report the best setting out ofall. The experiments start with keyword-based models and are followed byattention-based, named entity-based, transformers-based, and ensemble models.Our best approach, an ensemble model, achieves an F1 of 0.684 in thecompetition's evaluation phase.

UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models