论文标题
聆听受影响社区的界定语音：数据集和实验
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments
论文作者
论文摘要
基于当前关于多语言仇恨言论的工作（例如Ousidhoum等人（2019））和仇恨言论的减少（例如Sap等人（2020）），我们介绍了Xtremespeech，这是一个新的仇恨言论数据集，其中包含20,297个来自巴西，印度，印度，印度，印度和肯尼亚的社交媒体通道的20,297个。主要新颖性是，我们直接让受影响的社区收集和注释数据 - 而不是使公司和政府控制定义和打击仇恨言论。这种包容性的方法导致数据集更代表实际发生在线语音，并且很可能有助于删除边缘化社区认为造成最大危害的社交媒体内容。基于Xtremespeech，我们通过伴随的基线建立新的任务，提供了证据表明，由于国家之间的文化差异并对BERT的预测进行了可解释性分析，越野培训通常是不可行的。
Building on current work on multilingual hate speech (e.g., Ousidhoum et al. (2019)) and hate speech reduction (e.g., Sap et al. (2020)), we present XTREMESPEECH, a new hate speech dataset containing 20,297 social media passages from Brazil, Germany, India and Kenya. The key novelty is that we directly involve the affected communities in collecting and annotating the data - as opposed to giving companies and governments control over defining and combatting hate speech. This inclusive approach results in datasets more representative of actually occurring online speech and is likely to facilitate the removal of the social media content that marginalized communities view as causing the most harm. Based on XTREMESPEECH, we establish novel tasks with accompanying baselines, provide evidence that cross-country training is generally not feasible due to cultural differences between countries and perform an interpretability analysis of BERT's predictions.
