ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Chu Wang, Renshen Wang, Yasuhisa Fujii, Siyang Qin, Ashok Popat, Tomas Pfister
Abstract
Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial layout patterns of documents, they have limited ability to capture reading orders of given word-level node representations in a graph. We propose Reading Order Equivariant Positional Encoding (ROPE), a new positional encoding technique designed to apprehend the sequential presentation of words in documents. ROPE generates unique reading order codes for neighboring words relative to the target word given a word-level graph connectivity. We study two fundamental document entity extraction tasks including word labeling and word grouping on the public FUNSD dataset and a large-scale payment dataset. We show that ROPE consistently improves existing GCNs with a margin up to 8.4% F1-score.
BibTeX
@inproceedings{lee-etal-2021-rope,
title = "{ROPE}: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction",
author = "Lee, Chen-Yu and
Li, Chun-Liang and
Wang, Chu and
Wang, Renshen and
Fujii, Yasuhisa and
Qin, Siyang and
Popat, Ashok and
Pfister, Tomas",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.41/",
doi = "10.18653/v1/2021.acl-short.41",
pages = "314--321"
}