PLBR: A Semi-Supervised Document Key Information Extraction via Pseudo-Labeling Bias Rectification
Pengcheng Guo, Yonghong Song, Boyu Wang, Jiaohao Liu, Qi Zhang · IEEE Transactions on Knowledge and Data Engineering · 2024
Document key information extraction (DKIE) methods often require a large number of labeled samples, imposing substantial annotation costs in practical scenarios. Fortunately, pseudo-labeling based semi-supervised learning (PSSL) algorithms provide an effective paradigm to alleviate the reliance on labeled data by leveraging unlabeled data. However, the main challenges for PSSL in DKIE tasks: 1) context dependency of DKIE results in incorrect pseudo-labels. 2) high intra-class variance and low inter-class variation on DKIE. To this end, this paper proposes a similarity matrix Pseudo-Label Bias Rectification (PLBR) semi-supervised method for DKIE tasks, which improves the quality of pseudo-labels on DKIE benchmarks with rare labels. More specifically, the Similarity Matrix Bias Rectification (SMBR) module is proposed to improve the quality of pseudo-labels, which utilizes the contextual information of DKIE data through the analysis of similarity between labeled and unlabeled data. Moreover, a dual branch adaptive alignment (DBAA) mechanism is designed to adaptively align intra-class variance and alleviate inter-class variation on DKIE benchmarks, which is composed of two adaptive alignment ways. One is the intra-class alignment branch, which is designed to adaptively align intra-class variance. The other one is the inter-class alignment branch, which is developed to adaptively alleviate inter-class variance changes on the representation level. Extensive experiment results on two benchmarks demonstrate that PLBR achieves state-of-the-art performance and its performance surpasses the previous SOTA by$2.11\% \sim 2.53\%$,$2.09\% \sim 2.49\%$F1-score on FUNSD and CORD with rare labeled samples, respectively. Code will be open to the public.