提出首个即兴网络犯罪隐语检测框架，性能提升76倍。
Impromptu Cybercrime Euphemism Detection
- 构建上下文增强与多轮迭代训练的双阶段检测模型
- 在自建ICED数据集上实现76倍于旧方法的准确率提升
- 适合内容安全团队、平台风控与反诈研究者使用
即兴隐语检测对社交媒体内容安全至关重要，但现有方法在应对即兴隐语时表现不佳。本文首次探索即兴隐语检测问题，提出ICED数据集，并设计针对性检测框架。该框架包含粗粒度与细粒度分类模型：粗粒度模型剔除大部分无害内容；细粒度模型通过上下文增强与多轮迭代训练，更精准预测被掩码词的真实含义。利用ChatGPT评估模型能力，实验表明本方法相比此前最优模型实现76倍性能提升。
原文摘要 · Abstract (English)
Detecting euphemisms is essential for content security on various social media platforms, but existing methods designed for detecting euphemisms are ineffective in impromptu euphemisms. In this work, we make a first attempt to an exploration of impromptu euphemism detection and introduce the Impromptu Cybercrime Euphemisms Detection (ICED) dataset. Moreover, we propose a detection framework tailored to this problem, which employs context augmentation modeling and multi-round iterative training. Our detection framework mainly consists of a coarse-grained and a fine-grained classification model. The coarse-grained classification model removes most of the harmless content in the corpus to be detected. The fine-grained model, impromptu euphemisms detector, integrates context augmentation and multi-round iterations training to better predicts the actual meaning of a masked token. In addition, we leverage ChatGPT to evaluate the mode's capability. Experimental results demonstrate that our approach achieves a remarkable 76-fold improvement compared to the previous state-of-the-art euphemism detector.
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