用视觉增强提升仇恨言论检测的准确性和可信度
Trustworthy Hate Speech Detection Through Visual Augmentation
- 通过融合扩散图像增强语义信息，无需配对数据
- 在多个公开数据集上显著优于传统方法
- 适合关注模型可信度与多模态融合的研究者
社交媒体上仇恨言论的激增带来了严峻挑战，仇恨言论检测（HSD）日益关键。现有方法侧重于丰富上下文信息以提升检测性能，但忽略了仇恨言论固有的不确定性。我们提出一种新方法——可信仇恨言论检测视觉增强方法（TrusV-HSD），通过融合扩散视觉图像增强语义信息，并利用可信损失缓解不确定性。TrusV-HSD 在无配对数据条件下，通过多模态连接有效提取可信信息，学习语义表示。在多个公开的HSD数据集上的实验表明，该方法显著优于传统方法。
原文摘要 · Abstract (English)
The surge of hate speech on social media platforms poses a significant challenge, with hate speech detection~(HSD) becoming increasingly critical. Current HSD methods focus on enriching contextual information to enhance detection performance, but they overlook the inherent uncertainty of hate speech. We propose a novel HSD method, named trustworthy hate speech detection method through visual augmentation (TrusV-HSD), which enhances semantic information through integration with diffused visual images and mitigates uncertainty with trustworthy loss. TrusV-HSD learns semantic representations by effectively extracting trustworthy information through multi-modal connections without paired data. Our experiments on public HSD datasets demonstrate the effectiveness of TrusV-HSD, showing remarkable improvements over conventional methods.
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