@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",
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."
}
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%0 Conference Proceedings
%T ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
%A Lee, Chen-Yu
%A Li, Chun-Liang
%A Wang, Chu
%A Wang, Renshen
%A Fujii, Yasuhisa
%A Qin, Siyang
%A Popat, Ashok
%A Pfister, Tomas
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S 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)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F lee-etal-2021-rope
%X 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.
%R 10.18653/v1/2021.acl-short.41
%U https://aclanthology.org/2021.acl-short.41/
%U https://doi.org/10.18653/v1/2021.acl-short.41
%P 314-321
Markdown (Informal)
[ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction](https://aclanthology.org/2021.acl-short.41/) (Lee et al., ACL-IJCNLP 2021)
ACL