Abstract
Multiclass hate speech detection across demographic categories remains computationally challenging due to implicit targeting strategies and linguistic variability in social media content. Existing approaches rely solely on learned representations from training data, without explicitly incorporating structured ontological frameworks that can enhance classification through formal domain knowledge integration. We propose RoBERTa-OTA, which introduces ontology-guided attention mechanisms that process textual features alongside structured knowledge representations through enhanced Graph Convolutional Networks. The architecture combines RoBERTa embeddings with scaled attention layers and graph neural networks to integrate contextual language understanding with domain-specific semantic knowledge. Evaluation across 39,747 balanced samples using 5-fold cross-validation demonstrates significant performance gains over baseline RoBERTa implementations and existing state-of-the-art methods. RoBERTa-OTA achieves 96.04\% accuracy compared to 95.02\% for standard RoBERTa, with substantial improvements for challenging categories: gender-based hate speech detection improves by 2.36 percentage points while other hate speech categories improve by 2.38 percentage points. The enhanced architecture maintains computational efficiency with only 0.33\% parameter overhead, providing practical advantages for large-scale content moderation applications requiring fine-grained demographic hate speech classification.
Abstract (translated)
多类别仇恨言论检测在不同的人口统计类别之间仍然面临着计算上的挑战，主要是由于隐性的目标策略和社交媒体内容中的语言变化。现有的方法仅依赖于从训练数据中学到的表示，而没有明确地引入结构化的本体框架来通过正式领域知识的整合来增强分类效果。我们提出了RoBERTa-OTA，这是一种引入了基于本体指导注意机制的方法，该机制处理文本特征的同时结合了结构化知识表示，并通过改进后的图卷积网络实现这一过程。这种架构将RoBERTa嵌入与扩展的注意力层和图形神经网络相结合，从而将上下文语言理解与特定领域的语义知识整合起来。 在使用5倍交叉验证评估的39,747个平衡样本上进行的测试表明，相较于标准RoBERTa实现及现有最先进的方法，RoBERTa-OTA展示了显著的性能提升。RoBERTa-OTA实现了96.04%的准确率，而标准RoBERTa则为95.02%，对于性别仇恨言论检测和其他仇恨言论类别分别提高了2.36和2.38个百分点。 改进后的架构在仅增加0.33%参数开销的情况下保持了计算效率，这为需要进行细致的人口统计仇恨言论分类的大规模内容审核应用提供了实用优势。
URL
https://arxiv.org/abs/2603.04414
