This paper presents GeoContrastNet, a language-agnostic framework to
structured document understanding (DU) by integrating a contrastive learning
objective with graph attention networks (GATs), emphasizing the significant
role of geometric features. We propose a novel methodology that combines
geometric edge features with visual features within an overall two-staged
GAT-based framework, demonstrating promising results in both link prediction
and semantic entity recognition performance. Our findings reveal that combining
both geometric and visual features could match the capabilities of large DU
models that rely heavily on Optical Character Recognition (OCR) features in
terms of performance accuracy and efficiency. This approach underscores the
critical importance of relational layout information between the named text
entities in a semi-structured layout of a page. Specifically, our results
highlight the model's proficiency in identifying key-value relationships within
the FUNSD dataset for forms and also discovering the spatial relationships in
table-structured layouts for RVLCDIP business invoices. Our code and pretrained
models will be accessible on our official GitHub.

GeoContrastNet 是一个语言不可知的框架，通过将对比学习目标与图注意力网络（GATs）相结合，强调几何特征的重要作用，实现了结构化文档理解（DU）。我们提出了一种新的方法，将几何边缘特征与视觉特征结合在一个整体的基于 GAT 的框架中，在链接预测和语义实体识别性能方面展示了有希望的结果。我们的研究结果表明，将几何特征和视觉特征相结合，可以匹配那些在性能准确性和效率上严重依赖光学字符识别（OCR）特征的大型 DU 模型的能力。这种方法强调了页面的半结构化布局中命名文本实体之间的关系布局信息的重要性。具体而言，我们的结果突出了该模型在识别 FUNSD 数据集中的表单中的键 - 值关系以及在 RVLCDIP 业务发票的表格结构布局中发现空间关系的能力。我们的代码和预训练模型将在我们的官方 GitHub 上提供。

GeoContrastNet：用于语言无关文档理解的对比键 - 值边缘学习

GeoContrastNet: Contrastive Key-Value Edge Learning for  Language-Agnostic Document Understanding

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 this https URL.

我们提出了一种简单而有效的语言无关布局变换器 LiLT，可在单个语言的结构化文档上进行预训练，然后使用相应的现成单 / 多语言预训练文本模型对其他语言进行直接微调，实验结果表明 LiLT 在八种语言上均能够取得竞争性甚至卓越的性能，从而使得能够从文档布局结构的预训练中获得语言无关的益处。