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The proliferation of online hate speech has necessitated the creation ofalgorithms which can detect toxicity. Most of the past research focuses on thisdetection as a classification task, but assigning an absolute toxicity label isoften tricky. Hence, few of the past works transform the same task into aregression. This paper shows the comparative evaluation of differenttransformers and traditional machine learning models on a recently releasedtoxicity severity measurement dataset by Jigsaw. We further demonstrate theissues with the model predictions using explainability analysis.

Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments