Multimodal pre-training with text, layout, and image has achieved SOTA
performance for visually-rich document understanding tasks recently, which
demonstrates the great potential for joint learning across different
modalities. In this paper, we present LayoutXLM, a multimodal pre-trained model
for multilingual document understanding, which aims to bridge the language
barriers for visually-rich document understanding. To accurately evaluate
LayoutXLM, we also introduce a multilingual form understanding benchmark
dataset named XFUND, which includes form understanding samples in 7 languages
(Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and
key-value pairs are manually labeled for each language. Experiment results show
that the LayoutXLM model has significantly outperformed the existing SOTA
cross-lingual pre-trained models on the XFUND dataset. The pre-trained
LayoutXLM model and the XFUND dataset are publicly available at
this https URL

本文提出了一种基于 LayoutXLM 的多模态预训练模型，用于多语言文档理解，并在名为 XFUND 的多语言表单理解基准数据集上进行了验证，结果表明 LayoutXLM 模型在跨语言预训练方面优于现有的 SOTA 模型。