FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction-Reference-Cited by-同舟云学术

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FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction

Published:2022 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Author:

Lee Chen-Yu,Li Chun-Liang,Dozat Timothy,Perot Vincent,Su Guolong,Hua Nan,Ainslie Joshua,Wang Renshen,Fujii Yasuhisa,Pfister Tomas

Publisher

Association for Computational Linguistics

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Self-supervised commonsense knowledge learning for document-level relation extraction;Expert Systems with Applications;2024-09

2. Deep learning approaches for information extraction from visually rich documents: datasets, challenges and methods;International Journal on Document Analysis and Recognition (IJDAR);2024-07-29

3. Towards privacy preserved document image classification: a comprehensive benchmark;International Journal on Document Analysis and Recognition (IJDAR);2024-06-18

4. Incorporating multivariate semantic association graphs into multimodal networks for information extraction from documents;The Journal of Supercomputing;2024-05-22

5. FieldSwap: Data Augmentation for Effective Form-Like Document Extraction;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

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