# Document-Level Relation Extraction with Reconstruction

arXiv: https://arxiv.org/abs/2012.11384 · HTML: https://mlsanity.com/paper/2012.11384/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 30 of 41)

- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction](https://mlsanity.com/paper/2302.08675/) (2023)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion](https://mlsanity.com/paper/2106.08657/) (2021)
- [Discriminative Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2106.01562/) (2021)
- [SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction](https://mlsanity.com/paper/2109.12093/) (2021)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://mlsanity.com/paper/2201.03335/) (2022)
- [Document-Level Relation Extraction with Sentences Importance Estimation and Focusing](https://mlsanity.com/paper/2204.12679/) (2022)
- [Discourse-level Relation Extraction via Graph Pooling](https://mlsanity.com/paper/2101.00124/) (2021)
- [A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling](https://mlsanity.com/paper/2210.08709/) (2022)
- [None Class Ranking Loss for Document-Level Relation Extraction](https://mlsanity.com/paper/2205.00476/) (2022)
- [LLM with Relation Classifier for Document-Level Relation Extraction](https://mlsanity.com/paper/2408.13889/) (2024)
- [Towards Better Document-level Relation Extraction via Iterative Inference](https://mlsanity.com/paper/2211.14470/) (2022)
- [FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction](https://mlsanity.com/paper/2403.01886/) (2024)
- [Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning](https://mlsanity.com/paper/2201.04826/) (2022)
- [Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling](https://mlsanity.com/paper/2211.05343/) (2022)
- [Rethinking Document-Level Relation Extraction: A Reality Check](https://mlsanity.com/paper/2306.08953/) (2023)
- [A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling](https://mlsanity.com/paper/2306.14806/) (2023)
- [A Comprehensive Survey of Document-level Relation Extraction (2016-2023)](https://mlsanity.com/paper/2309.16396/) (2023)
- [Enhancing Document-level Relation Extraction by Entity Knowledge Injection](https://mlsanity.com/paper/2207.11433/) (2022)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [Anaphor Assisted Document-Level Relation Extraction](https://mlsanity.com/paper/2310.18604/) (2023)
- [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022)
- [Knowledge-Driven Cross-Document Relation Extraction](https://mlsanity.com/paper/2405.13546/) (2024)
- [Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception](https://mlsanity.com/paper/2105.08213/) (2021)
- [Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2109.05182/) (2021)
- [A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction](https://mlsanity.com/paper/2203.13953/) (2022)
- [NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction](https://mlsanity.com/paper/2204.00255/) (2022)
- [A Masked Image Reconstruction Network for Document-level Relation Extraction](https://mlsanity.com/paper/2204.09851/) (2022)

## Abstract (arXiv)

In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years. However, the learned graph representation universally models relation information between all entity pairs regardless of whether there are relationships between these entity pairs. Thus, those entity pairs without relationships disperse the attention of the encoder-classifier DocRE for ones with relationships, which may further hind the improvement of DocRE. To alleviate this issue, we propose a novel encoder-classifier-reconstructor model for DocRE. The reconstructor manages to reconstruct the ground-truth path dependencies from the graph representation, to ensure that the proposed DocRE model pays more attention to encode entity pairs with relationships in the training. Furthermore, the reconstructor is regarded as a relationship indicator to assist relation classification in the inference, which can further improve the performance of DocRE model. Experimental results on a large-scale DocRE dataset show that the proposed model can significantly improve the accuracy of relation extraction on a strong heterogeneous graph-based baseline.