  Recent efforts of multimodal Transformers have improved Visually Rich
Document Understanding (VrDU) tasks via incorporating visual and textual
information. However, existing approaches mainly focus on fine-grained elements
such as words and document image patches, making it hard for them to learn from
coarse-grained elements, including natural lexical units like phrases and
salient visual regions like prominent image regions. In this paper, we attach
more importance to coarse-grained elements containing high-density information
and consistent semantics, which are valuable for document understanding. At
first, a document graph is proposed to model complex relationships among
multi-grained multimodal elements, in which salient visual regions are detected
by a cluster-based method. Then, a multi-grained multimodal Transformer called
mmLayout is proposed to incorporate coarse-grained information into existing
pre-trained fine-grained multimodal Transformers based on the graph. In
mmLayout, coarse-grained information is aggregated from fine-grained, and then,
after further processing, is fused back into fine-grained for final prediction.
Furthermore, common sense enhancement is introduced to exploit the semantic
information of natural lexical units. Experimental results on four tasks,
including information extraction and document question answering, show that our
method can improve the performance of multimodal Transformers based on
fine-grained elements and achieve better performance with fewer parameters.
Qualitative analyses show that our method can capture consistent semantics in
coarse-grained elements.
