Visual Information Extraction (VIE) task aims to extract key information from
multifarious document images (e.g., invoices and purchase receipts). Most
previous methods treat the VIE task simply as a sequence labeling problem or
classification problem, which requires models to carefully identify each kind
of semantics by introducing multimodal features, such as font, color, layout.
But simply introducing multimodal features couldn't work well when faced with
numeric semantic categories or some ambiguous texts. To address this issue, in
this paper we propose a novel key-value matching model based on a graph neural
network for VIE (MatchVIE). Through key-value matching based on relevancy
evaluation, the proposed MatchVIE can bypass the recognitions to various
semantics, and simply focuses on the strong relevancy between entities.
Besides, we introduce a simple but effective operation, Num2Vec, to tackle the
instability of encoded values, which helps model converge more smoothly.
Comprehensive experiments demonstrate that the proposed MatchVIE can
significantly outperform previous methods. Notably, to the best of our
knowledge, MatchVIE may be the first attempt to tackle the VIE task by modeling
the relevancy between keys and values and it is a good complement to the
existing methods.

该研究提出了一种基于图神经网络的键值匹配模型 MatchVIE 用于视觉信息提取任务，通过关键词匹配和相关性评估实现了对实体之间的强相关度依赖，模型中采用的 Num2Vec 操作有助于提高编码值的稳定性和收敛性。该方法在实验中证明优于以往的方法，是现有方法的一个良好补充。