Multimodal pre-training with text, layout, and image has made significant
progress for Visually Rich Document Understanding (VRDU), especially the
fixed-layout documents such as scanned document images. While, there are still
a large number of digital documents where the layout information is not fixed
and needs to be interactively and dynamically rendered for visualization,
making existing layout-based pre-training approaches not easy to apply. In this
paper, we propose MarkupLM for document understanding tasks with markup
languages as the backbone, such as HTML/XML-based documents, where text and
markup information is jointly pre-trained. Experiment results show that the
pre-trained MarkupLM significantly outperforms the existing strong baseline
models on several document understanding tasks. The pre-trained model and code
will be publicly available at this https URL

本研究论文探讨了一种名为 MarkupLM 的预训练模型，它能够对 HTML/XML 等标记语言的文档进行理解和分析，相比现有的基于布局的预训练方法，在布局可交互和动态渲染的数字文档中有着更好的性能表现。实验证明，该预训练模型在多个文档理解任务上，比现有的强基线模型表现更优秀。