  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.
