论文标题
仇恨言论的法律方法：将欧盟的法律框架违反仇恨作为NLP任务的法律框架
A Legal Approach to Hate Speech: Operationalizing the EU's Legal Framework against the Expression of Hatred as an NLP Task
论文作者
论文摘要
我们提出了一种“法律方法”，以通过对邮政是否遵守刑事法的决定来遵守NLP任务来仇恨言论检测。比较现有的仇恨言论监管制度，我们基于欧盟的框架进行调查，因为它提供了广泛适用的法律最低标准。准确地判断帖子是否应受到惩罚，通常需要法律培训。我们表明，通过将法律评估分解为一系列简单的细分，即使是外行者也可以始终如一地注释。基于新注释的数据集，我们的实验表明，直接学习可惩罚内容的自动模型是具有挑战性的。但是，学习“目标群体”和“定位行为”的两个子任务，而不是端到端的惩罚性方法会产生更好的结果。总体而言，我们的方法还提供了比端到端模型更透明的决策，这是法律决策的关键点。
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 judging whether a post is punishable or not usually requires legal training. 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 an end-to-end approach to punishability 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.
