用多任务模型识别印英混杂社交文本中的假仇恨内容
Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head RoBERTa Model with Multi-Task Learning
- 双头RoBERTa模型并行处理真假仇恨判断与目标严重度预测
- 在混合语言数据上实现高精度分类，支持多任务联合优化
- 适合关注虚假仇恨信息检测的AI安全研究者使用
社交媒体虽促进全球连接，却也成为仇恨言论和虚假叙事快速传播的温床。本次Faux-Hate共享任务聚焦于由虚假叙事引发的仇恨言论（即假仇恨）检测，要求在印英混杂的社交媒体文本中识别此类现象。本文提出系统解决两个子任务：(a) 二分类的假仇恨检测（区分真实/虚假仇恨），(b) 目标与严重度预测（确定攻击对象及强度）。方法结合先进NLP技术与领域特定预训练，通过多任务学习提升性能。实验结果表明该系统在复杂语境下表现优异，验证了多任务学习在该问题上的有效性。
原文摘要 · Abstract (English)
Social media platforms, while enabling global connectivity, have become hubs for the rapid spread of harmful content, including hate speech and fake narratives \cite{davidson2017automated, shu2017fake}. The Faux-Hate shared task focuses on detecting a specific phenomenon: the generation of hate speech driven by fake narratives, termed Faux-Hate. Participants are challenged to identify such instances in code-mixed Hindi-English social media text. This paper describes our system developed for the shared task, addressing two primary sub-tasks: (a) Binary Faux-Hate detection, involving fake and hate speech classification, and (b) Target and Severity prediction, categorizing the intended target and severity of hateful content. Our approach combines advanced natural language processing techniques with domain-specific pretraining to enhance performance across both tasks. The system achieved competitive results, demonstrating the efficacy of leveraging multi-task learning for this complex problem.
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