# Discriminative Reasoning for Document-level Relation Extraction

arXiv: https://arxiv.org/abs/2106.01562 · HTML: https://mlsanity.com/paper/2106.01562/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling](https://mlsanity.com/paper/2010.11304/) (2020)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020)
- [Double Graph Based Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2009.13752/) (2020)
- [Attention Guided Graph Convolutional Networks for Relation Extraction](https://mlsanity.com/paper/1906.07510/) (2019)
- [Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://mlsanity.com/paper/1909.00228/) (2019)
- [Distant Supervision for Relation Extraction beyond the Sentence Boundary](https://mlsanity.com/paper/1609.04873/) (2016)
- [SG-Net: Syntax-Guided Machine Reading Comprehension](https://mlsanity.com/paper/1908.05147/) (2019)
- [Reinforcement Learning for Relation Classification from Noisy Data](https://mlsanity.com/paper/1808.08013/) (2018)
- [Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction](https://mlsanity.com/paper/1802.10569/) (2018)
- [Document-Level Relation Extraction with Reconstruction](https://mlsanity.com/paper/2012.11384/) (2020)
- [Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base](https://mlsanity.com/paper/2002.06115/) (2020)
- [Graph Neural Networks with Generated Parameters for Relation Extraction](https://mlsanity.com/paper/1902.00756/) (2019)
- [Neural Relation Extraction Within and Across Sentence Boundaries](https://mlsanity.com/paper/1810.05102/) (2018)
- [Syntax-Directed Attention for Neural Machine Translation](https://mlsanity.com/paper/1711.04231/) (2017)
- [Leveraging Dependency Forest for Neural Medical Relation Extraction](https://mlsanity.com/paper/1911.04123/) (2019)

## Built on by (most cited first, 25 of 25)

- [X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning](https://mlsanity.com/paper/2311.18799/) (2023)
- [SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction](https://mlsanity.com/paper/2109.12093/) (2021)
- [MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning](https://mlsanity.com/paper/2203.00357/) (2022)
- [Document-Level Relation Extraction with Sentences Importance Estimation and Focusing](https://mlsanity.com/paper/2204.12679/) (2022)
- [Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction](https://mlsanity.com/paper/2401.13598/) (2024)
- [Multi-hop Evidence Retrieval for Cross-document Relation Extraction](https://mlsanity.com/paper/2212.10786/) (2022)
- [RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction](https://mlsanity.com/paper/2310.15743/) (2023)
- [Exploring Self-supervised Logic-enhanced Training for Large Language Models](https://mlsanity.com/paper/2305.13718/) (2023)
- [LLM with Relation Classifier for Document-Level Relation Extraction](https://mlsanity.com/paper/2408.13889/) (2024)
- [Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction](https://mlsanity.com/paper/2305.11029/) (2023)
- [CorefDRE: Document-level Relation Extraction with coreference resolution](https://mlsanity.com/paper/2202.10744/) (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)
- [Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning](https://mlsanity.com/paper/2205.10511/) (2022)
- [DORE: Document Ordered Relation Extraction based on Generative Framework](https://mlsanity.com/paper/2210.16064/) (2022)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing](https://mlsanity.com/paper/2406.16529/) (2024)
- [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022)
- [TacoERE: Cluster-aware Compression for Event Relation Extraction](https://mlsanity.com/paper/2405.06890/) (2024)
- [Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?](https://mlsanity.com/paper/2310.00696/) (2023)
- [KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities](https://mlsanity.com/paper/2501.00571/) (2024)
- [Analyzing the Influence of Knowledge Graph Information on Relation Extraction](https://mlsanity.com/paper/2506.16343/) (2025)
- [Multi-Relation Extraction in Entity Pairs using Global Context](https://mlsanity.com/paper/2507.22926/) (2025)

## Abstract (arXiv)

Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the relation between one entity pair in a document. In this paper, we propose a novel discriminative reasoning framework to explicitly model the paths of these reasoning skills between each entity pair in this document. Thus, a discriminative reasoning network is designed to estimate the relation probability distribution of different reasoning paths based on the constructed graph and vectorized document contexts for each entity pair, thereby recognizing their relation. Experimental results show that our method outperforms the previous state-of-the-art performance on the large-scale DocRE dataset. The code is publicly available at https://github.com/xwjim/DRN.