A Multi-Task Semantic Decomposition Framework with Task-specific
  Pre-training for Few-Shot NER
Guanting Dong Zechen Wang Jinxu Zhao Gang Zhao Daichi Guo Dayuan Fu Tingfeng Hui Chen Zeng Keqing He Xuefeng Li Liwen Wang Xinyue Cui Weiran Xu
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.