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.

通过预训练的多模态模型和令牌路径预测，解决了扫描文档中实体命名识别的困难并提供了有效的信息提取解决方案。