  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.
