用少量标注数据快速识别文本异常，跨领域泛化能力强。
Anomaly Detection in Human Language via Meta-Learning: A Few-Shot Approach
- 采用元学习+原型网络，模拟少样本分类任务训练模型
- 在未见过的任务上仍保持高F1和AUC，优于主流基线
- 适合标注稀缺的场景，如诈骗、假新闻等新类型检测
我们提出一种元学习框架，用于在不同领域中以少量标注数据检测人类语言中的异常。语言异常（如垃圾短信、假新闻、仇恨言论）因稀疏性和多样性带来挑战。将异常检测视为少样本二分类问题，利用元学习训练可跨任务泛化的模型。在短信垃圾信息、新冠疫情假新闻、仇恨言论等数据集上评估，模型在未见任务上仅需极少标注异常样本即可快速适应。方法结合了事件式训练与原型网络，并引入领域重采样机制。实验表明，该方法在F1和AUC指标上均优于强基线。我们还开源代码与基准数据集，推动少样本文本异常检测研究。
原文摘要 · Abstract (English)
We propose a meta learning framework for detecting anomalies in human language across diverse domains with limited labeled data. Anomalies in language ranging from spam and fake news to hate speech pose a major challenge due to their sparsity and variability. We treat anomaly detection as a few shot binary classification problem and leverage meta-learning to train models that generalize across tasks. Using datasets from domains such as SMS spam, COVID-19 fake news, and hate speech, we evaluate model generalization on unseen tasks with minimal labeled anomalies. Our method combines episodic training with prototypical networks and domain resampling to adapt quickly to new anomaly detection tasks. Empirical results show that our method outperforms strong baselines in F1 and AUC scores. We also release the code and benchmarks to facilitate further research in few-shot text anomaly detection.
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