Papers › Document-level Relation Extraction with Context Guided Mention Integration and...
Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning
Chao Zhao, Daojian Zeng, Lu Xu, Jianhua Dai
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | CDR | CGM2IR-SciBERTbase | F1 | 73.8 | #5 of 10 | Archive leaderboard | report |
| Relation Extraction | DocRED | CGM2IR-RoBERTalarge | F1 | 63.89 | #7 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | CGM2IR-RoBERTalarge | Ign F1 | 61.96 | #7 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | CGM2IR-BERTbase | F1 | 62.06 | #18 of 62 | Archive leaderboard | report |
| Relation Extraction | DocRED | CGM2IR-BERTbase | Ign F1 | 60.24 | #18 of 62 | Archive leaderboard | report |
| Relation Extraction | GDA | CGM2IR-SciBERTbase | F1 | 84.7 | #6 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections