SOTAVerified

Document-level Relation Extraction

Document-level RE aim to identify the relations of various entity pairs expressed across multiple sentences.

Papers

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Showing 71–80 of 106 papers

TitleStatusHype
KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities—0
EIDER: Evidence-enhanced Document-level Relation Extraction—0
The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction—0
Mention-centered Graph Neural Network for Document-level Relation Extraction—0
Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction—0
Modular Self-Supervision for Document-Level Relation Extraction—0
Document-level Relation Extraction with Dual-tier Heterogeneous Graph—0
A Masked Image Reconstruction Network for Document-level Relation Extraction—0
NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction—0
Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning—0
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All datasetsBc8DocRED-IEDWIERe-DocRED

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioRex+DirectionalityEvaluation Macro F156.06—Unverified
#ModelMetricClaimedVerifiedStatus
1REXELRelation F160.1—Unverified
#ModelMetricClaimedVerifiedStatus
1VaeDiff-DocREF10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VaeDiff-DocREF10.79—Unverified