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

**Authors:** Jiapeng Wang, Lianwen Jin, Kai Ding  
**arXiv:** [2202.13669](https://arxiv.org/abs/2202.13669)  
**Date:** 2022-02-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2202.13669v1/

## Key points

- Effective models in structured document understanding are crucial for intelligent document processing.
- Existing models often have a limitation of being language-specific, particularly tailored towards English data during pre-training.
- The introduction of Language-independent Layout Transformer (LiLT) addresses this constraint by enabling pre-training on documents from a single language and fine-tuning on various languages using pre-trained textual models.
- LiLT has been demonstrated to achieve competitive or superior performance across eight different languages in diverse downstream benchmarks.
- LiLT's adaptability showcases its ability to transcend language barriers and leverage pre-training benefits in document layout structure across multiple linguistic contexts.
- Authors Jiapeng Wang, Lianwen Jin, and Kai Ding collaborated to develop LiLT, which was presented at the ACL 2022 Main Conference.
- The code and model for LiLT are accessible through the public repository at https://github.com/jpWang/LiLT, offering an opportunity for researchers and practitioners to explore this cutting-edge technology.

## Summary

In the realm of structured document understanding, effective models have been a focal point due to their essential role in intelligent document processing. However, a common limitation among existing models is their language specificity. They are often tailored towards English data during the pre-training phase. This constraint prompted the introduction of a groundbreaking solution known as Language-independent Layout Transformer (LiLT). This innovative model offers a straightforward yet powerful approach to structured document understanding by enabling pre-training on documents from a single language and subsequent fine-tuning on various languages using off-the-shelf monolingual or multilingual pre-trained textual models. The efficacy of LiLT was demonstrated through extensive experimentation across eight different languages, showcasing its ability to achieve competitive or even superior performance on diverse downstream benchmarks. This adaptability highlights LiLT's capacity to transcend language barriers and leverage the benefits of pre-training in document layout structure across multiple linguistic contexts. The collaborative efforts of authors Jiapeng Wang, Lianwen Jin, and Kai Ding culminated in the development of this transformative model, which was presented at the ACL 2022 Main Conference. For researchers and practitioners interested in exploring this cutting-edge technology, both the code and model for LiLT are readily accessible through the public repository at https://github.com/jpWang/LiLT. With its potential to revolutionize structured document understanding across different languages, LiLT stands as a testament to ongoing advancements in natural language processing and intelligent document analysis.