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

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arxiv 2104.08836 v3 pith:S3OU2PPY submitted 2021-04-18 cs.CL

Yiheng Xu , Tengchao Lv , Lei Cui , Guoxin Wang , Yijuan Lu , Dinei Florencio , Cha Zhang , Furu Wei This is my paper · ORCID
classification cs.CL
keywords layoutxlmunderstandingdocumentdatasetmodelmultilingualmultimodalpre-trained
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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.

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Forward citations

Cited by 6 Pith papers

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