  Self-augmentation has received increasing research interest recently to
improve named entity recognition (NER) performance in low-resource scenarios.
Token substitution and mixup are two feasible heterogeneous self-augmentation
techniques for NER that can achieve effective performance with certain
specialized efforts. Noticeably, self-augmentation may introduce potentially
noisy augmented data. Prior research has mainly resorted to heuristic
rule-based constraints to reduce the noise for specific self-augmentation
methods individually. In this paper, we revisit these two typical
self-augmentation methods for NER, and propose a unified meta-reweighting
strategy for them to achieve a natural integration. Our method is easily
extensible, imposing little effort on a specific self-augmentation method.
Experiments on different Chinese and English NER benchmarks show that our token
substitution and mixup method, as well as their integration, can achieve
effective performance improvement. Based on the meta-reweighting mechanism, we
can enhance the advantages of the self-augmentation techniques without much
extra effort.
