XFUND: A Multilingual Form Understanding Benchmark

Introduction

XFUND is a multilingual form understanding benchmark dataset that includes human-labeled forms with key-value pairs in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese).

Download

• XFUND v1.0: https://github.com/doc-analysis/XFUND/releases/tag/v1.0

Statistics

lang	split	header	question	answer	other	total
ZH	training	441	3,266	2,808	896	7,411
	testing	122	1,077	821	312	2,332
JA	training	229	3,692	4,641	1,666	10,228
	testing	58	1,253	1,732	586	3,629
ES	training	253	3,013	4,254	3,929	11,449
	testing	90	909	1,218	1,196	3,413
FR	training	183	2,497	3,427	2,709	8,816
	testing	66	1,023	1,281	1,131	3,501
IT	training	166	3,762	4,932	3,355	12,215
	testing	65	1,230	1,599	1,135	4,029
DE	training	155	2,609	3,992	1,876	8,632
	testing	59	858	1,322	650	2,889
PT	training	185	3,510	5,428	2,531	11,654
	testing	59	1,288	1,940	882	4,169

Baselines

For the code example, please refer to the LayoutXLM repository.

Results

Language-specific Finetuning

	Model	FUNSD	ZH	JA	ES	FR	IT	DE	PT	Avg.
Semantic Entity Recognition	xlm-roberta-base	0.667	0.8774	0.7761	0.6105	0.6743	0.6687	0.6814	0.6818	0.7047
	infoxlm-base	0.6852	0.8868	0.7865	0.6230	0.7015	0.6751	0.7063	0.7008	0.7207
	layoutxlm-base	0.794	0.8924	0.7921	0.7550	0.7902	0.8082	0.8222	0.7903	0.8056
Relation Extraction	xlm-roberta-base	0.2659	0.5105	0.5800	0.5295	0.4965	0.5305	0.5041	0.3982	0.4769
	infoxlm-base	0.2920	0.5214	0.6000	0.5516	0.4913	0.5281	0.5262	0.4170	0.4910
	layoutxlm-base	0.5483	0.7073	0.6963	0.6896	0.6353	0.6415	0.6551	0.5718	0.6432

Zero-shot Transfer Learning

	Model	FUNSD	ZH	JA	ES	FR	IT	DE	PT	Avg.
SER	xlm-roberta-base	0.667	0.4144	0.3023	0.3055	0.371	0.2767	0.3286	0.3936	0.3824
	infoxlm-base	0.6852	0.4408	0.3603	0.3102	0.4021	0.2880	0.3587	0.4502	0.4119
	layoutxlm-base	0.794	0.6019	0.4715	0.4565	0.5757	0.4846	0.5252	0.539	0.5561
RE	xlm-roberta-base	0.2659	0.1601	0.2611	0.2440	0.2240	0.2374	0.2288	0.1996	0.2276
	infoxlm-base	0.2920	0.2405	0.2851	0.2481	0.2454	0.2193	0.2027	0.2049	0.2423
	layoutxlm-base	0.5483	0.4494	0.4408	0.4708	0.4416	0.4090	0.3820	0.3685	0.4388

Multitask Fine-tuning

	Model	FUNSD	ZH	JA	ES	FR	IT	DE	PT	Avg.
SER	xlm-roberta-base	0.6633	0.883	0.7786	0.6223	0.7035	0.6814	0.7146	0.6726	0.7149
	infoxlm-base	0.6538	0.8741	0.7855	0.5979	0.7057	0.6826	0.7055	0.6796	0.7106
	layoutxlm-base	0.7924	0.8973	0.7964	0.7798	0.8173	0.821	0.8322	0.8241	0.8201
RE	xlm-roberta-base	0.3638	0.6797	0.6829	0.6828	0.6727	0.6937	0.6887	0.6082	0.6341
	infoxlm-base	0.3699	0.6493	0.6473	0.6828	0.6831	0.6690	0.6384	0.5763	0.6145
	layoutxlm-base	0.6671	0.8241	0.8142	0.8104	0.8221	0.8310	0.7854	0.7044	0.7823

Citation

If you find XFUND useful in your research, please cite the following paper:

@inproceedings{xu-etal-2022-xfund,
    title = "{XFUND}: A Benchmark Dataset for Multilingual Visually Rich Form Understanding",
    author = "Xu, Yiheng  and
      Lv, Tengchao  and
      Cui, Lei  and
      Wang, Guoxin  and
      Lu, Yijuan  and
      Florencio, Dinei  and
      Zhang, Cha  and
      Wei, Furu",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-acl.253",
    doi = "10.18653/v1/2022.findings-acl.253",
    pages = "3214--3224",
    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.",
}

License

The content of this project itself is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Portions of the source code are based on the transformers project. Microsoft Open Source Code of Conduct

Contact Information

For help or issues using XFUND, please submit a GitHub issue.

For other communications related to XFUND, please contact Lei Cui ([email protected]), Furu Wei ([email protected]).