  There is a growing demand in the field of KIE (Key Information Extraction) to
apply semi-supervised learning to save manpower and costs, as training document
data using fully-supervised methods requires labor-intensive manual annotation.
The main challenges of applying SSL in the KIE are (1) underestimation of the
confidence of tail classes in the long-tailed distribution and (2) difficulty
in achieving intra-class compactness and inter-class separability of tail
features. To address these challenges, we propose a novel semi-supervised
approach for KIE with Class-Rebalancing and Merged Semantic Pseudo-Labeling
(CRMSP). Firstly, the Class-Rebalancing Pseudo-Labeling (CRP) module introduces
a reweighting factor to rebalance pseudo-labels, increasing attention to tail
classes. Secondly, we propose the Merged Semantic Pseudo-Labeling (MSP) module
to cluster tail features of unlabeled data by assigning samples to Merged
Prototypes (MP). Additionally, we designed a new contrastive loss specifically
for MSP. Extensive experimental results on three well-known benchmarks
demonstrate that CRMSP achieves state-of-the-art performance. Remarkably, CRMSP
achieves 3.24% f1-score improvement over state-of-the-art on the CORD.
