Key-value relations are prevalent in Visually-Rich Documents (VRDs), often
depicted in distinct spatial regions accompanied by specific color and font
styles. These non-textual cues serve as important indicators that greatly
enhance human comprehension and acquisition of such relation triplets. However,
current document AI approaches often fail to consider this valuable prior
information related to visual and spatial features, resulting in suboptimal
performance, particularly when dealing with limited examples. To address this
limitation, our research focuses on few-shot relational learning, specifically
targeting the extraction of key-value relation triplets in VRDs. Given the
absence of a suitable dataset for this task, we introduce two new few-shot
benchmarks built upon existing supervised benchmark datasets. Furthermore, we
propose a variational approach that incorporates relational 2D-spatial priors
and prototypical rectification techniques. This approach aims to generate
relation representations that are more aware of the spatial context and unseen
relation in a manner similar to human perception. Experimental results
demonstrate the effectiveness of our proposed method by showcasing its ability
to outperform existing methods. This study also opens up new possibilities for
practical applications.

通过引入两个新的少样本基准数据集，本研究针对可视化丰富的文档（VRD）中的键 - 值关系三元组的提取，提出了一种变分方法，其中包含关系 2D 空间先验知识和原型化校正技术，实验结果表明该方法的有效性，并为实际应用开辟了新的可能性。