Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multimodal Pre-training Based on Graph Attention Network for Document
  Understanding   Document intelligence as a relatively new research topic supports many
business applications. Its main task is to automatically read, understand, and
analyze documents. However, due to the diversity of formats (invoices, reports,
forms, etc.) and layouts in documents, it is difficult to make machines
understand documents. In this paper, we present the GraphDoc, a multimodal
graph attention-based model for various document understanding tasks. GraphDoc
is pre-trained in a multimodal framework by utilizing text, layout, and image
information simultaneously. In a document, a text block relies heavily on its
surrounding contexts, so we inject the graph structure into the attention
mechanism to form a graph attention layer so that each input node can only
attend to its neighborhoods. The input nodes of each graph attention layer are
composed of textual, visual, and positional features from semantically
meaningful regions in a document image. We do the multimodal feature fusion of
each node by the gate fusion layer. The contextualization between each node is
modeled by the graph attention layer. GraphDoc learns a generic representation
from only 320k unlabeled documents via the Masked Sentence Modeling task.
Extensive experimental results on the publicly available datasets show that
GraphDoc achieves state-of-the-art performance, which demonstrates the
effectiveness of our proposed method.

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The categories of this paper are: math
15
The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn