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ViHOS: Hate Speech Spans Detection for Vietnamese

2023-01-24 17:53:21
Phu Gia Hoang, Canh Duc Luu, Khanh Quoc Tran, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

Abstract

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.

Abstract (translated)

对其他用户的仇恨和攻击性语言的增加是社交媒体平台使用增加的不良副作用之一。这可能会导致人类审核员难以审查通过分类系统的标记评论。为了解决这个问题,我们提供了ViHOS(越南仇恨和攻击性跨度)数据集,它是第一个由人类标注的 corpus,其中包含11k个评论中的26k个跨度。我们还提供了越南评论中的仇恨和攻击性跨度的定义以及详细的标注指南。此外,我们与其他最先进的模型进行了实验。具体而言,XLM-R$_{Large}$ 在单个跨度和所有跨度检测中取得了最佳的 F1 分数,而PhoBERT$_{Large}$ 在多个跨度检测中取得了最高的分数。最后,我们的错误分析表明,在对我们的数据中检测特定类型的跨度方面存在困难。声明:本文包含可能被视为粗俗、攻击性或骚扰的评论。

URL

https://arxiv.org/abs/2301.10186

PDF

https://arxiv.org/pdf/2301.10186.pdf


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