XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding

Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei


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. However, the existed research work has focused only on the English domain while neglecting the importance of multilingual generalization. In this paper, we introduce a human-annotated multilingual form understanding benchmark dataset named XFUND, which includes form understanding samples in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese). Meanwhile, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually rich document understanding. Experimental results show that the LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUND dataset. The XFUND dataset and the pre-trained LayoutXLM model have been publicly available at https://aka.ms/layoutxlm.
Anthology ID:
2022.findings-acl.253
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3214–3224
Language:
URL:
https://aclanthology.org/2022.findings-acl.253
DOI:
10.18653/v1/2022.findings-acl.253
Bibkey:
Cite (ACL):
Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, and Furu Wei. 2022. XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3214–3224, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (Xu et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.253.pdf
Software:
 2022.findings-acl.253.software.zip
Data
FUNSD