Abstract
Document Understanding is an evolving field in Natural Language Processing (NLP). In particular, visual and spatial features are essential in addition to the raw text itself and hence, several multimodal models were developed in the field of Visual Document Understanding (VDU). However, while research is mainly focused on Key Information Extraction (KIE), Relation Extraction (RE) between identified entities is still under-studied. For instance, RE is crucial to regroup entities or obtain a comprehensive hierarchy of data in a document. In this paper, we present a model that, initialized from LayoutLMv3, can match or outperform the current state-of-the-art results in RE applied to Visually-Rich Documents (VRD) on FUNSD and CORD datasets, without any specific pre-training and with fewer parameters. We also report an extensive ablation study performed on FUNSD, highlighting the great impact of certain features and modelization choices on the performances.
Abstract (translated)
文档理解是一个不断发展的领域，自然语言处理（NLP）中。特别是，视觉和空间特征在文本本身之外至关重要，因此，在视觉文档理解（VDU）领域中，已经开发了许多多模态模型。然而，研究主要集中在关键信息提取（KIE）上，而识别实体之间的关系扩展（RE）研究仍处于起步阶段。例如，RE对于重新分组实体或获得文档数据的全面层次结构非常重要。在本文中，我们提出了一个模型，最初基于LayoutLMv3，可以在不进行特定预训练的情况下，与FUNSD和CORD数据集中的当前最先进结果在RE上相匹配或超越，且参数更少。我们还对FUNSD进行了广泛的消融研究，突出了某些特征和模型化选择对性能的巨大影响。
URL
https://arxiv.org/abs/2404.10848
