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

• URL: http://arxiv.org/abs/2404.10848v1
• Date: Tue, 16 Apr 2024 18:50:57 GMT
• Title: A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents
• Authors: Wiam Adnan, Joel Tang, Yassine Bel Khayat Zouggari, Seif Edinne Laatiri, Laurent Lam, Fabien Caspani,
• Abstract summary: We present a model that can match or outperform the current state-of-the-art results in Relation Extraction (RE) applied to Visually-Rich Documents (VRD) We also report an extensive ablation study performed on FUNSD, highlighting the great impact of certain features and modelization choices on the performances.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• GEGA: Graph Convolutional Networks and Evidence Retrieval Guided Attention for Enhanced Document-level Relation Extraction [15.246183329778656]
  Document-level relation extraction (DocRE) aims to extract relations between entities from unstructured document text. To overcome these challenges, we propose GEGA, a novel model for DocRE. We evaluate the GEGA model on three widely used benchmark datasets: DocRED, Re-DocRED, and Revisit-DocRED.
  arXiv  Detail & Related papers  (2024-07-31T07:15:33Z)
• AutoRE: Document-Level Relation Extraction with Large Language Models [27.426703757501507]
  We introduce AutoRE, an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. Our experiments on the RE-DocRED dataset showcase AutoRE's best performance, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-03-21T23:48:21Z)
• Learning to Extract Structured Entities Using Language Models [52.281701191329]
  Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP metric.
  arXiv  Detail & Related papers  (2024-02-06T22:15:09Z)
• Enhancing Visually-Rich Document Understanding via Layout Structure Modeling [91.07963806829237]
  We propose GraphLM, a novel document understanding model that injects layout knowledge into the model. We evaluate our model on various benchmarks, including FUNSD, XFUND and CORD, and achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2023-08-15T13:53:52Z)
• Named Entity and Relation Extraction with Multi-Modal Retrieval [51.660650522630526]
  Multi-modal named entity recognition (NER) and relation extraction (RE) aim to leverage relevant image information to improve the performance of NER and RE. We propose a novel Multi-modal Retrieval based framework (MoRe) MoRe contains a text retrieval module and an image-based retrieval module, which retrieve related knowledge of the input text and image in the knowledge corpus respectively.
  arXiv  Detail & Related papers  (2022-12-03T13:11:32Z)
• ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select [53.071352033539526]
  We study the problem of extracting N-ary relations from scientific articles. Our proposed method ReSel decomposes this task into a two-stage procedure. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
  arXiv  Detail & Related papers  (2022-10-26T02:28:02Z)
• Modeling Multi-Granularity Hierarchical Features for Relation Extraction [26.852869800344813]
  We propose a novel method to extract multi-granularity features based solely on the original input sentences. We show that effective structured features can be attained even without external knowledge.
  arXiv  Detail & Related papers  (2022-04-09T09:44:05Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• Learning from Context or Names? An Empirical Study on Neural Relation Extraction [112.06614505580501]
  We study the effect of two main information sources in text: textual context and entity mentions (names) We propose an entity-masked contrastive pre-training framework for relation extraction (RE) Our framework can improve the effectiveness and robustness of neural models in different RE scenarios.
  arXiv  Detail & Related papers  (2020-10-05T11:21:59Z)
• Reasoning with Latent Structure Refinement for Document-Level Relation Extraction [20.308845516900426]
  We propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED)
  arXiv  Detail & Related papers  (2020-05-13T13:36:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.