LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding

• URL: http://arxiv.org/abs/2305.18721v2
• Date: Fri, 9 Jun 2023 03:29:43 GMT
• Title: LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding
• Authors: Yi Tu, Ya Guo, Huan Chen, Jinyang Tang
• Abstract summary: This paper proposes a novel multi-modal pre-training model, LayoutMask. It can enhance the interactions between text and layout modalities in a unified model. It can achieve state-of-the-art results on a wide variety of VrDU problems.
• Score: 7.7514466231699455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visually-rich Document Understanding (VrDU) has attracted much research attention over the past years. Pre-trained models on a large number of document images with transformer-based backbones have led to significant performance gains in this field. The major challenge is how to fusion the different modalities (text, layout, and image) of the documents in a unified model with different pre-training tasks. This paper focuses on improving text-layout interactions and proposes a novel multi-modal pre-training model, LayoutMask. LayoutMask uses local 1D position, instead of global 1D position, as layout input and has two pre-training objectives: (1) Masked Language Modeling: predicting masked tokens with two novel masking strategies; (2) Masked Position Modeling: predicting masked 2D positions to improve layout representation learning. LayoutMask can enhance the interactions between text and layout modalities in a unified model and produce adaptive and robust multi-modal representations for downstream tasks. Experimental results show that our proposed method can achieve state-of-the-art results on a wide variety of VrDU problems, including form understanding, receipt understanding, and document image classification.

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