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 61–70 of 106 papers

TitleStatusHype
GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction—0
GEGA: Graph Convolutional Networks and Evidence Retrieval Guided Attention for Enhanced Document-level Relation Extraction—0
Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning—0
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction—0
HIN: Hierarchical Inference Network for Document-Level Relation Extraction—0
Improving Distantly Supervised Document-Level Relation Extraction Through Natural Language Inference—0
DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level Relation Extraction—0
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning—0
Key Mention Pairs Guided Document-Level Relation Extraction—0
KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities—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