  Documents that consist of diverse templates and exhibit complex spatial
structures pose a challenge for document entity classification. We propose
KNN-former, which incorporates a new kind of spatial bias in attention
calculation based on the K-nearest-neighbor (KNN) graph of document entities.
We limit entities' attention only to their local radius defined by the KNN
graph. We also use combinatorial matching to address the one-to-one mapping
property that exists in many documents, where one field has only one
corresponding entity. Moreover, our method is highly parameter-efficient
compared to existing approaches in terms of the number of trainable parameters.
Despite this, experiments across various datasets show our method outperforms
baselines in most entity types. Many real-world documents exhibit combinatorial
properties which can be leveraged as inductive biases to improve extraction
accuracy, but existing datasets do not cover these documents. To facilitate
future research into these types of documents, we release a new ID document
dataset that covers diverse templates and languages. We also release enhanced
annotations for an existing dataset.
