StrucTexT: Structured Text Understanding with Multi-Modal Transformers

• URL: http://arxiv.org/abs/2108.02923v2
• Date: Tue, 10 Aug 2021 03:44:20 GMT
• Title: StrucTexT: Structured Text Understanding with Multi-Modal Transformers
• Authors: Yulin Li and Yuxi Qian and Yuchen Yu and Xiameng Qin and Chengquan Zhang and Yan Liu and Kun Yao and Junyu Han and Jingtuo Liu and Errui Ding
• Abstract summary: Structured text understanding on Visually Rich Documents (VRDs) is a crucial part of Document Intelligence. This paper proposes a unified framework named StrucTexT, which is flexible and effective for handling both sub-tasks. We evaluate our method for structured text understanding at segment-level and token-level and show it outperforms the state-of-the-art counterparts.
• Score: 29.540122964399046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structured text understanding on Visually Rich Documents (VRDs) is a crucial part of Document Intelligence. Due to the complexity of content and layout in VRDs, structured text understanding has been a challenging task. Most existing studies decoupled this problem into two sub-tasks: entity labeling and entity linking, which require an entire understanding of the context of documents at both token and segment levels. However, little work has been concerned with the solutions that efficiently extract the structured data from different levels. This paper proposes a unified framework named StrucTexT, which is flexible and effective for handling both sub-tasks. Specifically, based on the transformer, we introduce a segment-token aligned encoder to deal with the entity labeling and entity linking tasks at different levels of granularity. Moreover, we design a novel pre-training strategy with three self-supervised tasks to learn a richer representation. StrucTexT uses the existing Masked Visual Language Modeling task and the new Sentence Length Prediction and Paired Boxes Direction tasks to incorporate the multi-modal information across text, image, and layout. We evaluate our method for structured text understanding at segment-level and token-level and show it outperforms the state-of-the-art counterparts with significantly superior performance on the FUNSD, SROIE, and EPHOIE datasets.

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