With the proliferation of social media, there has been a sharp increase in offensive content, particularly targeting vulnerable groups, exacerbating social problems such as hatred, racism, and sexism. Detecting offensive language use is crucial to prevent offensive language from being widely shared on social media. However, the accurate detection of irony, implication, and various forms of hate speech on social media remains a challenge. Natural language-based deep learning models require extensive training with large, comprehensive, and labeled datasets. Unfortunately, manually creating such datasets is both costly and error-prone. Additionally, the presence of human-bias in offensive language datasets is a major concern for deep learning models. In this paper, we propose a linguistic data augmentation approach to reduce bias in labeling processes, which aims to mitigate the influence of human bias by leveraging the power of machines to improve the accuracy and fairness of labeling processes. This approach has the potential to improve offensive language classification tasks across multiple languages and reduce the prevalence of offensive content on social media.

翻译：随着社交媒体的普及，攻击性内容急剧增加，尤其是针对弱势群体的内容，加剧了仇恨、种族主义和性别歧视等社会问题。检测攻击性语言的使用对于防止其在社交媒体上广泛传播至关重要。然而，准确检测社交媒体上的讽刺、暗示和各种形式的仇恨言论仍然是一个挑战。基于自然语言的深度学习模型需要大量、全面且带标签的数据集进行广泛训练。不幸的是，手动创建此类数据集既昂贵又易出错。此外，攻击性语言数据集中人类偏见的存在是深度学习模型的主要担忧。在本文中，我们提出了一种语言数据增强方法，以减少标注过程中的偏见，旨在通过利用机器的力量来减轻人类偏见的影响，从而提高标注过程的准确性和公平性。这种方法具有改善多种语言攻击性语言分类任务、减少社交媒体上攻击性内容流行程度的潜力。