LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding

• URL: http://arxiv.org/abs/2104.08836v1
• Date: Sun, 18 Apr 2021 12:16:00 GMT
• Title: LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
• Authors: Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei
• Abstract summary: Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document understanding tasks. We present a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding.
• Score: 34.42574051786547
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 XFUN, 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 XFUN dataset. The pre-trained LayoutXLM model and the XFUN dataset will be publicly available at https://aka.ms/layoutxlm.

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