# Value Retrieval with Arbitrary Queries for Form-like Documents

arXiv: https://arxiv.org/abs/2112.07820 · HTML: https://mlsanity.com/paper/2112.07820/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [DocFormer: End-to-End Transformer for Document Understanding](https://mlsanity.com/paper/2106.11539/) (2021)
- [Attend, Copy, Parse -- End-to-end information extraction from documents](https://mlsanity.com/paper/1812.07248/) (2018)
- [A Span Extraction Approach for Information Extraction on Visually-Rich Documents](https://mlsanity.com/paper/2106.00978/) (2021)
- [Field Extraction from Forms with Unlabeled Data](https://mlsanity.com/paper/2110.04282/) (2021)
- [Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks](https://mlsanity.com/paper/2110.04413/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Question-Answering Approach to Key Value Pair Extraction from Form-like Document Images](https://mlsanity.com/paper/2304.07957/) (2023)
- [QueryForm: A Simple Zero-shot Form Entity Query Framework](https://mlsanity.com/paper/2211.07730/) (2022)
- [PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts](https://mlsanity.com/paper/2307.10551/) (2023)
- [End-to-End Document Classification and Key Information Extraction using Assignment Optimization](https://mlsanity.com/paper/2306.00750/) (2023)
- [UniVIE: A Unified Label Space Approach to Visual Information Extraction from Form-like Documents](https://mlsanity.com/paper/2401.09220/) (2024)

## Abstract (arXiv)

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.