DAViD: Domain Adaptive Visually-Rich Document Understanding with Synthetic Insights

ACL ARR 2024 December Submission1410 Authors

Abstract: Visually Rich Documents (VRDs), encompassing elements like charts, tables, and references, convey complex information across various fields. However, extracting information from these documents is labour-intensive, especially given their inconsistent formats and domain-specific requirements. While pretrained models for VRD Understanding have progressed, their reliance on large, annotated datasets limits scalability. This paper introduces the Domain Adaptive Visually-rich Document Understanding (DAViD) framework, which utilises machine-generated synthetic data for domain adaptation. DAViD integrates fine-grained and coarse-grained document representation learning and employs synthetic annotations to reduce the need for costly manual labelling. By leveraging pretrained models and synthetic data, DAViD achieves competitive performance with minimal annotated datasets. Extensive experiments validate DAViD’s effectiveness, demonstrating its ability to efficiently adapt to domain-specific VRDU tasks.

Paper Type: Long

Research Area: Information Extraction

Research Area Keywords: Visually Rich Document, Synthetic Data, Key Information Extraction, Multimodal

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 1410