# Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning

arXiv: https://arxiv.org/abs/2201.04826 · HTML: https://mlsanity.com/paper/2201.04826/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Exact solutions to the nonlinear dynamics of learning in deep linear neural networks](https://mlsanity.com/paper/1312.6120/) (2013)
- [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)
- [Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling](https://mlsanity.com/paper/2010.11304/) (2020)
- [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)
- [Coreferential Reasoning Learning for Language Representation](https://mlsanity.com/paper/2004.06870/) (2020)
- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://mlsanity.com/paper/1909.00228/) (2019)
- [Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction](https://mlsanity.com/paper/2102.10249/) (2021)
- [Distant Supervision for Relation Extraction beyond the Sentence Boundary](https://mlsanity.com/paper/1609.04873/) (2016)
- [Learning Deep Bilinear Transformation for Fine-grained Image Representation](https://mlsanity.com/paper/1911.03621/) (2019)
- [Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://mlsanity.com/paper/1906.04684/) (2019)
- [Global-to-Local Neural Networks for Document-Level Relation Extraction](https://mlsanity.com/paper/2009.10359/) (2020)
- [Fine-tune Bert for DocRED with Two-step Process](https://mlsanity.com/paper/1909.11898/) (2019)
- [HIN: Hierarchical Inference Network for Document-Level Relation Extraction](https://mlsanity.com/paper/2003.12754/) (2020)
- [Document-Level Relation Extraction with Reconstruction](https://mlsanity.com/paper/2012.11384/) (2020)
- [PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction](https://mlsanity.com/paper/2106.09895/) (2021)
- [SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2106.01709/) (2021)
- [Discriminative Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2106.01562/) (2021)
- [Learning Logic Rules for Document-level Relation Extraction](https://mlsanity.com/paper/2111.05407/) (2021)

## Built on by (most cited first, 4 of 4)

- [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://mlsanity.com/paper/2407.21489/) (2024)
- [FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction](https://mlsanity.com/paper/2403.01886/) (2024)
- [Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling](https://mlsanity.com/paper/2211.05343/) (2022)
- [Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier](https://mlsanity.com/paper/2501.05155/) (2025)

## Abstract (arXiv)

Document-level Relation Extraction (DRE) aims to recognize the relations between two entities. The entity may correspond to multiple mentions that span beyond sentence boundary. Few previous studies have investigated the mention integration, which may be problematic because coreferential mentions do not equally contribute to a specific relation. Moreover, prior efforts mainly focus on reasoning at entity-level rather than capturing the global interactions between entity pairs. In this paper, we propose two novel techniques, Context Guided Mention Integration and Inter-pair Reasoning (CGM2IR), to improve the DRE. Instead of simply applying average pooling, the contexts are utilized to guide the integration of coreferential mentions in a weighted sum manner. Additionally, inter-pair reasoning executes an iterative algorithm on the entity pair graph, so as to model the interdependency of relations. We evaluate our CGM2IR model on three widely used benchmark datasets, namely DocRED, CDR, and GDA. Experimental results show that our model outperforms previous state-of-the-art models.