Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich
  Document Understanding   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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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: nlin
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th