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Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer.

Tengfei Liu1, Yongli Hu1, Junbin Gao2

  • 1Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

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|April 23, 2024
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Summary

This study introduces a novel Hierarchical Prompt and Multi-modal Transformer (HPMT) for classifying long documents with images. The HPMT method effectively captures hierarchical features and multi-modal associations, outperforming existing techniques.

Keywords:
Multi-modal long document classificationMulti-modal transformerMulti-scale multi-modal transformerPrompt learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Existing long document classification (LDC) methods primarily focus on unimodal text data.
  • The potential of multi-modal documents, incorporating both text and images, remains underexplored in LDC.

Purpose of the Study:

  • To propose an innovative approach for multi-modal long document classification.
  • To address the limitations of unimodal LDC by incorporating image data.

Main Methods:

  • Introduced the Hierarchical Prompt and Multi-modal Transformer (HPMT) for multi-modal LDC.
  • Developed a Multi-scale Multi-modal Transformer (MsMMT) to capture correlations between sentences and images at various granularities.
  • Incorporated multi-scale convolutional kernels on sentence features within MsMMT.
  • Designed a Hierarchical Prompt (HierPrompt) block for cross-level information interaction using section-level and sentence-level prompts.

Main Results:

  • The HPMT method demonstrated superior performance in multi-modal long document classification.
  • Achieved significant performance advantages over existing techniques on four challenging datasets.
  • The model effectively captures hierarchical structural features and complex multi-modal associations.

Conclusions:

  • The proposed HPMT method is highly effective for multi-modal long document classification.
  • Integrating hierarchical prompts and multi-modal transformers enhances the understanding of complex document structures.
  • This approach opens new avenues for research in multi-modal document analysis.