LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding

Jiapeng Wang, Lianwen Jin, Kai Ding


Abstract
Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure. Code and model are publicly available at https://github.com/jpWang/LiLT.
Anthology ID:
2022.acl-long.534
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7747–7757
Language:
URL:
https://aclanthology.org/2022.acl-long.534
DOI:
10.18653/v1/2022.acl-long.534
Bibkey:
Cite (ACL):
Jiapeng Wang, Lianwen Jin, and Kai Ding. 2022. LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7747–7757, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (Wang et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.534.pdf
Code
 jpwang/lilt +  additional community code
Data
CORDEPHOIEFUNSDRVL-CDIPXFUND