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 41–50 of 106 papers

TitleStatusHype
On the Robustness of Document-Level Relation Extraction Models to Entity Name VariationsCode0
Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation ExtractionCode0
Adaptive Hinge Balance Loss for Document-Level Relation ExtractionCode0
Multi-view Inference for Relation Extraction with Uncertain KnowledgeCode0
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple ExtractionCode0
Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention PairsCode0
HistRED: A Historical Document-Level Relation Extraction DatasetCode0
Consistent Document-Level Relation Extraction via CounterfactualsCode0
A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete LabelingCode0
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented GraphsCode0
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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