@inproceedings{das-etal-2022-one,
title = "Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments",
author = "Das, Millon and
Saha, Punyajoy and
Das, Mithun",
editor = "Kumar, Ritesh and
Ojha, Atul Kr. and
Zampieri, Marcos and
Malmasi, Shervin and
Kadar, Daniel",
booktitle = "Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.trac-1.2",
pages = "10--15",
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.",
}
Markdown (Informal)
[Which One Is More Toxic? Findings from Jigsaw Rate Severity of Toxic Comments](https://aclanthology.org/2022.trac-1.2) (Das et al., TRAC 2022)
ACL