The objective of few-shot named entity recognition is to identify named
entities with limited labeled instances. Previous works have primarily focused
on optimizing the traditional token-wise classification framework, while
neglecting the exploration of information based on NER data characteristics. To
address this issue, we propose a Multi-Task Semantic Decomposition Framework
via Joint Task-specific Pre-training (MSDP) for few-shot NER. Drawing
inspiration from demonstration-based and contrastive learning, we introduce two
novel pre-training tasks: Demonstration-based Masked Language Modeling (MLM)
and Class Contrastive Discrimination. These tasks effectively incorporate
entity boundary information and enhance entity representation in Pre-trained
Language Models (PLMs). In the downstream main task, we introduce a multi-task
joint optimization framework with the semantic decomposing method, which
facilitates the model to integrate two different semantic information for
entity classification. Experimental results of two few-shot NER benchmarks
demonstrate that MSDP consistently outperforms strong baselines by a large
margin. Extensive analyses validate the effectiveness and generalization of
MSDP.

通过多任务预训练的多任务语义分解框架，通过引入基于示范和对比学习的两种新型预训练任务（示范式遮蔽语言建模和类对比判别），能够有效地融入实体边界信息并加强预训练语言模型中的实体表示。在下游主任务中，通过语义分解方法引入多任务联合优化框架，使模型能够整合两种不同的语义信息进行实体分类，实验证明该方法在两个少样本命名实体识别基准测试中始终优于强基线模型，并通过广泛的分析验证了其有效性和泛化能力。