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.

翻译：本文提出了一种新的基于问答（QA）的关键值对提取方法——KVPFormer，用于从类表单文档图像中鲁棒地提取实体间的关键值关系。具体而言，KVPFormer首先利用Transformer编码器从图像中的所有实体中识别出关键实体，然后将这些关键实体作为**问题**，并行输入到Transformer解码器中，预测其对应的**答案**（即值实体）。为了实现更高的答案预测准确性，我们进一步提出了一种从粗到细的答案预测方法：在粗阶段为每个已识别问题提取多个候选答案，随后在细阶段从这些候选中选择最可能的答案。通过这种方式，可以有效降低答案预测的学习难度，从而提高预测准确率。此外，我们在模型的自注意力/交叉注意力机制中引入了空间兼容性注意力偏置，以更好地建模实体间的空间交互。结合这些新技术，我们提出的KVPFormer在FUNSD和XFUND数据集上取得了最先进的结果，F1分数分别比先前最优方法提升了7.2%和13.2%。