# A Question-Answering Approach to Key Value Pair Extraction from Form-like Document Images Source: [A Question-Answering Approach to Key Value Pair Extraction from Form-like Document Images]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:13:37.852Z Authors: Kai Hu, Zhuoyuan Wu, Zhuoyao Zhong, Weihong Lin, Lei Sun, Qiang Huo Published: 2023-04-17T02:55:31.000Z ## Abstract In this paper, we present a new question-answering (QA) based key-value pair extraction approach, called KVPFormer, to robustly extracting key-value relationships between entities from form-like document images. Specifically, KVPFormer first identifies key entities from all entities in an image with a Transformer encoder, then takes these key entities as \\textbf{questions} and feeds them into a Transformer decoder to predict their corresponding \\textbf{answers} (i.e., value entities) in parallel. To achieve higher answer prediction accuracy, we propose a coarse-to-fine answer prediction approach further, which first extracts multiple answer candidates for each identified question in the coarse stage and then selects the most likely one among these candidates in the fine stage. In this way, the learning difficulty of answer prediction can be effectively reduced so that the prediction accuracy can be improved. Moreover, we introduce a spatial compatibility attention bias into the self-attention/cross-attention mechanism for \\Ours{} to better model the spatial interactions between entities. With these new techniques, our proposed \\Ours{} achieves state-of-the-art results on FUNSD and XFUND datasets, outperforming the previous best-performing method by 7.2\\% and 13.2\\% in F1 score, respectively. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()