Relation Extraction On Webnlg

Results

Performance results of various models on this benchmark

Model Name	F1	Paper Title	Repository
TPLinker	91.9	TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
NovelTagging	28.3	Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
PFN	93.6	A Partition Filter Network for Joint Entity and Relation Extraction
CGT(UniLM)	83.4	Contrastive Triple Extraction with Generative Transformer	-
HBT (CasRel)	91.8	A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
ETL-Span	83.1	Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
RIN (BERT, K=2)	90.1	Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations	-
UniRel	94.7	UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
TDEER	93.1	TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations
RSAN	82.1	A Relation-Specific Attention Network for Joint Entity and Relation Extraction
CopyRE MultiDecoder	37.1	Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
CopyRE' OneDecoder	60.5	CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning
RIFRE	92.6	Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction
SPN	93.4	Joint Entity and Relation Extraction with Set Prediction Networks

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