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.

我们提出了一种新的基于K最近邻图的空间偏差的注意力计算方法 KNN-former，该方法限制实体的注意力仅在K最近邻图定义的局部半径内；我们还使用组合匹配来解决多对一映射的问题，此外，与现有方法相比，我们的方法在可训练参数的数量方面更加高效。虽然如此，在各种数据集上的实验证明，我们的方法在大多数实体类型上优于基线方法。许多现实世界的文档具有组合特性，可以作为归纳偏差来提高提取准确性，但现有数据集没有涵盖这些文档。为了促进未来对这些类型的文档的研究，我们发布了一个涵盖多样化模板和语言的新ID文档数据集，并对现有数据集进行了增强的注释。

轻量级空间建模以从文件中提取组合信息