MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding

Junlong Li, Yiheng Xu, Lei Cui, Furu Wei


Abstract
Multimodal pre-training with text, layout, and image has made significant progress for Visually Rich Document Understanding (VRDU), especially the fixed-layout documents such as scanned document images. While, there are still a large number of digital documents where the layout information is not fixed and needs to be interactively and dynamically rendered for visualization, making existing layout-based pre-training approaches not easy to apply. In this paper, we propose MarkupLM for document understanding tasks with markup languages as the backbone, such as HTML/XML-based documents, where text and markup information is jointly pre-trained. Experiment results show that the pre-trained MarkupLM significantly outperforms the existing strong baseline models on several document understanding tasks. The pre-trained model and code will be publicly available at https://aka.ms/markuplm.
Anthology ID:
2022.acl-long.420
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6078–6087
Language:
URL:
https://aclanthology.org/2022.acl-long.420
DOI:
10.18653/v1/2022.acl-long.420
Bibkey:
Cite (ACL):
Junlong Li, Yiheng Xu, Lei Cui, and Furu Wei. 2022. MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6078–6087, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding (Li et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.420.pdf