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Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing




Published: 2021 Juli
Verlag: NAACL 2022
Organisation: The 6th Workshop on Online Abuse and Harms (2022)
BibTeX

Kurzfassung
Research to tackle hate speech plaguing online media has made strides in providing solutions, analyzing bias and curating data. A challeng- ing problem is ambiguity between hate speech and offensive language, causing low perfor- mance both overall and specifically for the hate speech class. It can be argued that misclas- sifying actual hate speech content as merely offensive can lead to further harm against tar- geted groups. In our work, we mitigate this potentially harmful phenomenon by proposing an adversarial debiasing method to separate the two classes. We show that our method works for English, Arabic German and Hindi, plus in a multilingual setting, improving performance over baselines.

Download: Media:adversarial_debiasing.pdf



Forschungsgruppe

Web Science


Forschungsgebiet

Natürliche Sprachverarbeitung