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|Abstract=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. | |Abstract=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=2022.woah-1.1.pdf | |Download=2022.woah-1.1.pdf | ||
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|Forschungsgruppe=Web Science | |Forschungsgruppe=Web Science | ||
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Aktuelle Version vom 14. Oktober 2022, 08:56 Uhr
Published: 2022
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:2022.woah-1.1.pdf