HMS-BERT：混合多任务自训练用于多语言和多标签网络欺凌检测
HMS-BERT: Hybrid Multi-Task Self-Training for Multilingual and Multi-Label Cyberbullying Detection
AI总结：
本文提出HMS-BERT框架，结合预训练多语言BERT与手工特征，实现多语言多标签网络欺凌检测，通过自训练策略提升低资源语言性能，实验显示在多标签任务中宏F1达0.9847。
摘要：
社交媒体上的网络欺凌本质上是多语言且多方面的，其中攻击行为常跨越多个类别。现有方法通常受限于单语言假设或单任务模型，难以应对现实中的多语言和多标签场景。本文提出HMS-BERT，一种混合多任务自训练框架，基于预训练多语言BERT，整合上下文表示与手工语言特征，联合优化细粒度多标签滥用分类任务和三类主分类任务。为解决低资源语言标注数据稀缺问题，引入迭代自训练策略与基于置信度的伪标签，促进跨语言知识迁移。实验表明，HMS-BERT在四个公开数据集上表现优异，多标签任务宏F1达0.9847，主分类任务准确率达0.6775。消融研究进一步验证了所提组件的有效性。
英文摘要：
Cyberbullying on social media is inherently multilingual and multi-faceted, where abusive behaviors often overlap across multiple categories. Existing methods are commonly limited by monolingual assumptions or single-task formulations, which restrict their effectiveness in realistic multilingual and multi-label scenarios. In this paper, we propose HMS-BERT, a hybrid multi-task self-training framework for multilingual and multi-label cyberbullying detection. Built upon a pretrained multilingual BERT backbone, HMS-BERT integrates contextual representations with handcrafted linguistic features and jointly optimizes a fine-grained multi-label abuse classification task and a three-class main classification task. To address labeled data scarcity in low-resource languages, an iterative self-training strategy with confidence-based pseudo-labeling is introduced to facilitate cross-lingual knowledge transfer. Experiments on four public datasets demonstrate that HMS-BERT achieves strong performance, attaining a macro F1-score of up to 0.9847 on the multi-label task and an accuracy of 0.6775 on the main classification task. Ablation studies further verify the effectiveness of the proposed components.
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。
