用提示词+对抗增强，让少样本仇恨言论检测更准更快。
Few-shot Hate Speech Detection Based on the MindSpore Framework
- 引入可学习提示词和注意力池化网络，提升模型泛化能力。
- 在HateXplain和HSOL数据集上F1分数超越基线模型。
- 适合资源有限环境下快速部署的仇恨言论检测系统。
社交媒体上仇恨言论的泛滥对在线社区构成严重威胁，亟需有效的检测系统。尽管深度学习模型表现出潜力，但在少样本或低资源场景下，其性能常因依赖大规模标注数据而下降。为此，我们提出基于MindSpore平台的MS-FSLHate框架，采用可学习提示词嵌入、带有注意力池化的CNN-BiLSTM主干网络，并结合同义词对抗数据增强，以提升模型泛化能力。在两个基准数据集HateXplain和HSOL上的实验表明，该方法在精确率、召回率和F1分数上均优于现有基线模型。此外，该框架展现出高效率与良好可扩展性，适用于资源受限环境下的部署。研究结果表明，将提示学习与对抗增强结合，可实现鲁棒且适应性强的少样本仇恨言论检测。
原文摘要 · Abstract (English)
The proliferation of hate speech on social media poses a significant threat to online communities, requiring effective detection systems. While deep learning models have shown promise, their performance often deteriorates in few-shot or low-resource settings due to reliance on large annotated corpora. To address this, we propose MS-FSLHate, a prompt-enhanced neural framework for few-shot hate speech detection implemented on the MindSpore deep learning platform. The model integrates learnable prompt embeddings, a CNN-BiLSTM backbone with attention pooling, and synonym-based adversarial data augmentation to improve generalization. Experimental results on two benchmark datasets-HateXplain and HSOL-demonstrate that our approach outperforms competitive baselines in precision, recall, and F1-score. Additionally, the framework shows high efficiency and scalability, suggesting its suitability for deployment in resource-constrained environments. These findings highlight the potential of combining prompt-based learning with adversarial augmentation for robust and adaptable hate speech detection in few-shot scenarios.
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