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
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2141–2146
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.187
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.187.pdf
- Code
- salesforce/qvr-simpledlm