DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents
Mingfei Gao, Le Xue, Chetan Ramaiah, Chen Xing, Ran Xu, Caiming Xiong
Abstract
We propose, DocQueryNet, a value retrieval method with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of the layout and semantics of a form. To further boost model performance, we propose a simple document language modeling (SimpleDLM) strategy to improve document understanding on large-scale model pre-training. Experimental results show that DocQueryNet outperforms previous designs significantly and the SimpleDLM further improves our performance on value retrieval by around 17% F1 score compared with the state-of-the-art pre-training method. Code is available here, https://github.com/salesforce/QVR-SimpleDLM.- Anthology ID:
- 2022.coling-1.187
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2141–2146
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.187
- DOI:
- Bibkey:
- Cite (ACL):
- Mingfei Gao, Le Xue, Chetan Ramaiah, Chen Xing, Ran Xu, and Caiming Xiong. 2022. DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2141–2146, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents (Gao et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.187.pdf
- Code
- salesforce/qvr-simpledlm
Export citation
@inproceedings{gao-etal-2022-docquerynet,
title = "{D}oc{Q}uery{N}et: Value Retrieval with Arbitrary Queries for Form-like Documents",
author = "Gao, Mingfei and
Xue, Le and
Ramaiah, Chetan and
Xing, Chen and
Xu, Ran and
Xiong, Caiming",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.187",
pages = "2141--2146",
abstract = "We propose, DocQueryNet, a value retrieval method with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of the layout and semantics of a form. To further boost model performance, we propose a simple document language modeling (SimpleDLM) strategy to improve document understanding on large-scale model pre-training. Experimental results show that DocQueryNet outperforms previous designs significantly and the SimpleDLM further improves our performance on value retrieval by around 17{\%} F1 score compared with the state-of-the-art pre-training method. Code is available here, \url{https://github.com/salesforce/QVR-SimpleDLM}.",
}
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%0 Conference Proceedings %T DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents %A Gao, Mingfei %A Xue, Le %A Ramaiah, Chetan %A Xing, Chen %A Xu, Ran %A Xiong, Caiming %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F gao-etal-2022-docquerynet %X We propose, DocQueryNet, a value retrieval method with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of the layout and semantics of a form. To further boost model performance, we propose a simple document language modeling (SimpleDLM) strategy to improve document understanding on large-scale model pre-training. Experimental results show that DocQueryNet outperforms previous designs significantly and the SimpleDLM further improves our performance on value retrieval by around 17% F1 score compared with the state-of-the-art pre-training method. Code is available here, https://github.com/salesforce/QVR-SimpleDLM. %U https://aclanthology.org/2022.coling-1.187 %P 2141-2146
Markdown (Informal)
[DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents](https://aclanthology.org/2022.coling-1.187) (Gao et al., COLING 2022)
- DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents (Gao et al., COLING 2022)
ACL
- Mingfei Gao, Le Xue, Chetan Ramaiah, Chen Xing, Ran Xu, and Caiming Xiong. 2022. DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2141–2146, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.