Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
Submission Type: Regular Long Paper
Submission Track: Speech and Multimodality
Submission Track 2: Information Extraction
Keywords: visually-rich document understanding, information extraction, named entity recognition
TL;DR: Introducing Token Path Prediction, a simple method to address reading order issues in information extraction from visually-rich documents, achieving SOTA performance in various VrD tasks including NER, entity linking and reading order prediction.
Abstract: 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.
Submission Number: 1698
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