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Recent years have witnessed the rise and success of pre-training techniquesin visually-rich document understanding. However, most existing methods lackthe systematic mining and utilization of layout-centered knowledge, leading tosub-optimal performances. In this paper, we propose ERNIE-Layout, a noveldocument pre-training solution with layout knowledge enhancement in the wholeworkflow, to learn better representations that combine the features from text,layout, and image. Specifically, we first rearrange input sequences in theserialization stage, and then present a correlative pre-training task, readingorder prediction, to learn the proper reading order of documents. To improvethe layout awareness of the model, we integrate a spatial-aware disentangledattention into the multi-modal transformer and a replaced regions predictiontask into the pre-training phase. Experimental results show that ERNIE-Layoutachieves superior performance on various downstream tasks, setting newstate-of-the-art on key information extraction, document image classification,and document question answering datasets. The code and models are publiclyavailable athttp://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-layout.

ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding