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• W3204285087 abstract "We propose a ‘legal approach’ to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task. Comparing existing regulatory regimes for hate speech, we base our investigation on the European Union’s framework as it provides a widely applicable legal minimum standard. Accurately deciding whether a post is punishable or not usually requires legal education. We show that, by breaking the legal assessment down into a series of simpler sub-decisions, even laypersons can annotate consistently. Based on a newly annotated dataset, our experiments show that directly learning an automated model of punishable content is challenging. However, learning the two sub-tasks of ‘target group’ and ‘targeting conduct’ instead of a holistic, end-to-end approach to the legal assessment yields better results. Overall, our method also provides decisions that are more transparent than those of end-to-end models, which is a crucial point in legal decision-making." @default.
• W3204285087 created "2021-10-11" @default.
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• W3204285087 date "2022-01-01" @default.
• W3204285087 modified "2023-09-24" @default.
• W3204285087 title "A Legal Approach to Hate Speech – Operationalizing the EU’s Legal Framework against the Expression of Hatred as an NLP Task" @default.
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• W3204285087 doi "https://doi.org/10.18653/v1/2022.nllp-1.5" @default.
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