Relation Extraction On Conll04

Metrics

RE+ Micro F1

Results

Performance results of various models on this benchmark

		Paper Title
ReLiK-Large	78.1	ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
ASP+T0-3B	76.3	Autoregressive Structured Prediction with Language Models
REBEL	75.4	REBEL: Relation Extraction By End-to-end Language generation
Table-Sequence	73.6	Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
TANL	72.6	Structured Prediction as Translation between Augmented Natural Languages
TablERT	72.6	Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized Representations
TriMF	72.35	A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
SpERT	71.47	Span-based Joint Entity and Relation Extraction with Transformer Pre-training
Deeper	71.08	Deeper Task-Specificity Improves Joint Entity and Relation Extraction
Multi-turn QA	68.9	Entity-Relation Extraction as Multi-Turn Question Answering
Global	67.8	End-to-End Neural Relation Extraction with Global Optimization
Table Representation	61	-
multi-head + AT	-	Adversarial training for multi-context joint entity and relation extraction
Relation-Metric with AT	-	Neural Metric Learning for Fast End-to-End Relation Extraction
Biaffine attention	-	End-to-end neural relation extraction using deep biaffine attention
multi-head	-	Joint entity recognition and relation extraction as a multi-head selection problem

0 of 16 row(s) selected.

Relation Extraction On Conll04 | SOTA | HyperAI