多类仇恨言论检测与RoBERTa-OTA：整合Transformer注意力与图卷积网络
Multiclass Hate Speech Detection with RoBERTa-OTA: Integrating Transformer Attention and Graph Convolutional Networks
AI总结：
RoBERTa-OTA通过整合Transformer注意力与图卷积网络，提升多类仇恨言论检测的准确率与效率。
摘要：
跨人口统计学类别的多类仇恨言论检测仍然由于隐含的目标策略和社交媒体内容的语言变异性而具有计算挑战性。现有方法仅依赖于训练数据中学习的表示，而没有显式地结合结构化的本体框架，这些框架可以通过形式化的领域知识整合来增强分类。我们提出了RoBERTa-OTA，它引入了本体引导的注意力机制，通过增强的图卷积网络处理文本特征和结构化知识表示。该架构结合了RoBERTa嵌入和缩放注意力层以及图神经网络，以整合上下文语言理解与领域特定的语义知识。使用5折交叉验证在39,747个平衡样本上评估，证明了RoBERTa-OTA在基准RoBERTa实现和现有最先进方法上的显著性能提升。RoBERTa-OTA达到96.04%的准确率，相比标准RoBERTa的95.02%，在具有挑战性的类别中有所改进：基于性别的仇恨言论检测提高了2.36个百分点，其他仇恨言论类别提高了2.38个百分点。增强的架构在参数上只有0.33%的开销，为需要细粒度人口统计学仇恨言论分类的大型内容管理应用提供了实用优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
