Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs), in which named entity recognition (NER) is treated as a sequence-labeling task of predicting the BIO entity tags for tokens, following the typical setting of NLP. However, BIO-tagging scheme relies on the correct order of model inputs, which is not guaranteed in real-world NER on scanned VrDs where text are recognized and arranged by OCR systems. Such reading order issue hinders the accurate marking of entities by BIO-tagging scheme, making it impossible for sequence-labeling methods to predict correct named entities. To address the reading order issue, we introduce Token Path Prediction (TPP), a simple prediction head to predict entity mentions as token sequences within documents. Alternative to token classification, TPP models the document layout as a complete directed graph of tokens, and predicts token paths within the graph as entities. For better evaluation of VrD-NER systems, we also propose two revised benchmark datasets of NER on scanned documents which can reflect real-world scenarios. Experiment results demonstrate the effectiveness of our method, and suggest its potential to be a universal solution to various information extraction tasks on documents.

翻译：多模态预训练模型的最新进展显著提升了从视觉丰富文档（VrDs）中提取信息的能力，其中命名实体识别（NER）被视作序列标注任务，遵循自然语言处理的典型设置，通过预测令牌的BIO实体标签来实现。然而，BIO标注方案依赖于模型输入的正确顺序，这在扫描文档的NER实际场景中无法保证——因为OCR系统识别的文本排列顺序可能不准确。这种阅读顺序问题会阻碍BIO标注方案对实体进行精确标记，导致序列标注方法无法预测正确的命名实体。为了解决阅读顺序问题，我们提出令牌路径预测（TPP）——一种简单的预测头，用于将文档中的实体提及预测为令牌序列。作为令牌分类的替代方案，TPP将文档布局建模为令牌的完全有向图，并预测该图中的令牌路径作为实体。为更好地评估VrD-NER系统，我们还提出了两个修订后的扫描文档NER基准数据集，能够反映真实场景。实验结果表明了该方法的有效性，并暗示其作为文档中各类信息提取任务通用解决方案的潜力。