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

2022-06-27 13:20:24
Millon Madhur Das, Punyajoy Saha, Mithun Das
arXiv_CL
           
Abstract

The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. Hence, few of the past works transform the same task into a regression. This paper shows the comparative evaluation of different transformers and traditional machine learning models on a recently released toxicity severity measurement dataset by Jigsaw. We further demonstrate the issues with the model predictions using explainability analysis.

Abstract (translated)

URL

https://arxiv.org/abs/2206.13284

PDF

https://arxiv.org/pdf/2206.13284.pdf

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