LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (2202.13669v1)

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.

• The paper introduces LiLT, which decouples text and layout flows to pre-train on monolingual data for multilingual document understanding.
• It employs a bi-directional attention complementation mechanism and multi-task objectives (MVLM, KPL, CAI) to capture intrinsic layout-text relationships.
• Empirical evaluations show LiLT outperforms state-of-the-art models like LayoutXLM in both fine-tuning and zero-shot settings across eight languages.

Insights into LiLT: A Language-Independent Layout Transformer for Structured Document Understanding

The paper presents the Language-independent Layout Transformer (LiLT), a novel approach in the domain of structured document understanding (SDU). Leveraging the independence of document layouts from linguistic content, LiLT demonstrates that document structure can be effectively pre-trained in a monolingual context before being fine-tuned for multilingual applications.

Key Contributions

1. Language-Independence: Unlike typical SDU models that require multilingual data pre-training, LiLT can pre-train on monolingual data and fine-tune across languages. This is particularly advantageous given the scarcity of labeled multilingual datasets.
2. Model Architecture: The architecture of LiLT involves separate text and layout flows, utilizing a bi-directional attention complementation mechanism (BiACM) to enhance interaction between these modalities. This design facilitates effective cross-modality interaction, ensuring that the layout information is leveraged independently of language.
3. Pre-training Objectives: LiLT employs a combination of tasks—Masked Visual-LLMing (MVLM), Key Point Location (KPL), and Cross-modal Alignment Identification (CAI)—to effectively learn joint representations. This strategy enables the model to better capture the intrinsic relationships within document layouts.
4. Practicality and Efficiency: By focusing on layout structure pre-training using a monolingual dataset, LiLT simplifies the pre-training stage, significantly reducing the resources and effort typically required for multilingual document data collection and preparation.

Empirical Evaluation

The paper provides comprehensive experimental validation across eight languages using multiple settings: language-specific fine-tuning, zero-shot transfer, and multitask fine-tuning. Remarkably, LiLT demonstrates competitive or superior performance compared to state-of-the-art models, including LayoutXLM, which relies heavily on multilingual pre-training.

• Language-Specific Fine-Tuning: LiLT outperforms existing models on various datasets such as FUNSD, CORD, and EPHOIE by decoupling and effectively recombining text and layout features during fine-tuning.
• Zero-Shot Transfer: LiLT efficiently transfers knowledge from pre-training in English to other languages, surpassing counterparts in accuracy, even when the multilingual data was not seen during pre-training.
• Multitask Fine-Tuning: The model shows improved performance over individual language tuning, highlighting the benefit of captured layout patterns that transcend language barriers.

Implications and Future Directions

The introduction of LiLT speaks volumes about the potential for decoupling layout and textual information in document understanding. By abstracting document structure from language, researchers can achieve more flexible and resource-efficient models capable of scaling across languages.

The implications for real-world applications are substantial. Industries relying on document processing, such as finance, healthcare, and logistics, can greatly benefit from more adaptable AI solutions that do not necessitate extensive multilingual datasets for training.

Future research may explore further optimizations in layout and text flow interactions, potentially enhancing LiLT’s applicability to non-textual or low-resource languages. Furthermore, integrating generalized visual information remains an open avenue for boosting performance in visually complex documents.

Overall, LiLT marks a significant step forward in the pursuit of truly language-agnostic document understanding systems, setting a foundation for future advancements in AI-driven document intelligence.