Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing prototype-based sequence labeling models tend to memorize entity mentions which would be easily confused by close prototypes. In this paper, we proposed a Prototypical Semantic Decoupling method via joint Contrastive learning (PSDC) for few-shot NER. Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference. Besides, we further introduce joint contrastive learning objectives to better integrate two kinds of decoupling information and prevent semantic collapse. Experimental results on two few-shot NER benchmarks demonstrate that PSDC consistently outperforms the previous SOTA methods in terms of overall performance. Extensive analysis further validates the effectiveness and generalization of PSDC.

翻译：小样本命名实体识别（Few-shot NER）旨在仅基于少量标注样本识别命名实体。现有大多数基于原型的序列标注模型容易记忆实体提及，而相近的原型会导致这些模型产生混淆。本文提出了一种基于联合对比学习的原型语义解耦方法（PSDC）用于小样本NER。具体而言，我们通过两种掩码策略将类别特定原型和上下文语义原型进行解耦，引导模型关注两种不同的语义信息进行推理。此外，我们进一步引入联合对比学习目标，以更好地整合两类解耦信息并防止语义坍塌。在两个小样本NER基准上的实验结果表明，PSDC在整体性能上持续优于现有最优方法（SOTA）。进一步的分析验证了PSDC的有效性和泛化能力。