mlsanityData to 7 Oct 2026

A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents

arXiv 2404.10848 ↗First posted 16 Apr 2024cs.CL cs.AI cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Document Understanding is an evolving field in Natural Language Processing (NLP). In particular, visual and spatial features are essential in addition to the raw text itself and hence, several multimodal models were developed in the field of Visual Document Understanding (VDU). However, while research is mainly focused on Key Information Extraction (KIE), Relation Extraction (RE) between identified entities is still under-studied. For instance, RE is crucial to regroup entities or obtain a comprehensive hierarchy of data in a document. In this paper, we present a model that, initialized from LayoutLMv3, can match or outperform the current state-of-the-art results in RE applied to Visually-Rich Documents (VRD) on FUNSD and CORD datasets, without any specific pre-training and with fewer parameters. We also report an extensive ablation study performed on FUNSD, highlighting the great impact of certain features and modelization choices on the performances.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

19 of 24 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 549
  3. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 481
  4. 2020LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understandingcited by 406
  5. 2019FUNSD: A Dataset for Form Understanding in Noisy Scanned Documentscited by 345
  6. 2020K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapterscited by 318
  7. 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 271
  8. 2020Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Poolingcited by 115
  9. 2021BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documentscited by 104
  10. 2021StrucTexT: Structured Text Understanding with Multi-Modal Transformerscited by 90
  11. 2021An Improved Baseline for Sentence-level Relation Extractioncited by 80
  12. 2022DeepStruct: Pretraining of Language Models for Structure Predictioncited by 45
7 more
  1. 2023GeoLayoutLM: Geometric Pre-training for Visual Information Extractioncited by 40
  2. 2023DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extractioncited by 37
  3. 2020DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understandingcited by 31
  4. 2019Joint Entity Linking with Deep Reinforcement Learningcited by 27
  5. 2021Entity Relation Extraction as Dependency Parsing in Visually Rich Documentscited by 23
  6. 2020Revising FUNSD dataset for key-value detection in document imagescited by 10
  7. 2017Deep Residual Learning for Weakly-Supervised Relation Extractioncited by 7

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Efficient Medical VIE via Reinforcement Learningcited by 2