We propose value retrieval 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 our method 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 at https://github.com/salesforce/QVR-SimpleDLM.
@article{arxiv.2112.07820,
title = {Value Retrieval with Arbitrary Queries for Form-like Documents},
author = {Mingfei Gao and Le Xue and Chetan Ramaiah and Chen Xing and Ran Xu and Caiming Xiong},
journal= {arXiv preprint arXiv:2112.07820},
year = {2022}
}