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 21–30 of 106 papers

TitleStatusHype
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and GenesCode1
A sequence-to-sequence approach for document-level relation extractionCode1
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge DistillationCode1
Learning Logic Rules for Document-level Relation ExtractionCode1
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation ExtractionCode1
MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation ExtractionCode1
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage FusionCode1
Document-level Relation Extraction as Semantic SegmentationCode1
Discriminative Reasoning for Document-level Relation ExtractionCode1
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation ExtractionCode1
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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