Relational understanding is critical for a number of visually-rich documents
(VRDs) understanding tasks. Through multi-modal pre-training, recent studies
provide comprehensive contextual representations and exploit them as prior
knowledge for downstream tasks. In spite of their impressive results, we
observe that the widespread relational hints (e.g., relation of key/value
fields on receipts) built upon contextual knowledge are not excavated yet. To
mitigate this gap, we propose DocReL, a Document Relational Representation
Learning framework. The major challenge of DocReL roots in the variety of
relations. From the simplest pairwise relation to the complex global structure,
it is infeasible to conduct supervised training due to the definition of
relation varies and even conflicts in different tasks. To deal with the
unpredictable definition of relations, we propose a novel contrastive learning
task named Relational Consistency Modeling (RCM), which harnesses the fact that
existing relations should be consistent in differently augmented positive
views. RCM provides relational representations which are more compatible to the
urgent need of downstream tasks, even without any knowledge about the exact
definition of relation. DocReL achieves better performance on a wide variety of
VRD relational understanding tasks, including table structure recognition, key
information extraction and reading order detection.

本研究提出了 DocReL，一种文本关系表示学习框架，其中包含一种新的对比学习任务 Relational Consistency Modeling（RCM），并利用了上下文信息来改进视觉丰富文档中关系理解，如表结构识别，关键信息提取和阅读顺序检测。