LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
It addresses language barriers in document understanding for multilingual applications, but is incremental as it builds on existing multimodal pre-training approaches.
The paper tackles multilingual visually-rich document understanding by introducing LayoutXLM, a multimodal pre-trained model, and a new benchmark dataset XFUND in 7 languages, with results showing it significantly outperforms existing SOTA cross-lingual models on this dataset.
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 https://aka.ms/layoutxlm.