The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k comments. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Besides, we conduct experiments with various state-of-the-art models. Specifically, XLM-R$_{Large}$ achieved the best F1-scores in Single span detection and All spans detection, while PhoBERT$_{Large}$ obtained the highest in Multiple spans detection. Finally, our error analysis demonstrates the difficulties in detecting specific types of spans in our data for future research. Disclaimer: This paper contains real comments that could be considered profane, offensive, or abusive.
翻译:针对其他用户的仇恨和攻击性语言的上升,是社会网络平台使用增多的不利副作用之一。这可能使人类主持人难以审查分类系统过滤的贴标签评论。为了解决这一问题,我们提供了ViHOS(越南仇恨和攻击性spans)数据集,这是第一个在11k条评论中包含26k字的附加说明的数据集。我们还在越南的评论中给出了仇恨和攻击性跨度的定义,以及详细的说明性准则。此外,我们还与各种最先进的模型进行实验。具体来说,XLM-R$ ⁇ Large}$在单一波段探测和所有波段探测中达到了最佳F1分数,而PhoBERT$ ⁇ Large}则在多波段探测中获得了最高分数。最后,我们的错误分析表明我们数据中难以发现特定类型的跨度,供未来研究。不言而论者说:本文包含真实的评论,可以被视为直接、攻击或滥用。