Semantic Role Labeling On Ontonotes

Results

Performance results of various models on this benchmark

Model Name	F1	Paper Title	Repository
He et al.	82.1	Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
BiLSTM-Span	86.2	A Span Selection Model for Semantic Role Labeling
RoBERTa+RegCCRF	87.51	Constraining Linear-chain CRFs to Regular Languages	-
HeSyFu	88.59	-	-
CRF2o + BERT	87.66	Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
BiLSTM-Span (Ensemble)	87.0	A Span Selection Model for Semantic Role Labeling
ReCAT(pretrained on wikitext103)	88.0	Augmenting Transformers with Recursively Composed Multi-grained Representations	-
RoBERTa+CRF	87.27	Constraining Linear-chain CRFs to Regular Languages	-
CRF2o	83.66	Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
He et al.,	85.5	Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
MRC-SRL	88.3	An MRC Framework for Semantic Role Labeling
Tan et al.	82.7	Deep Semantic Role Labeling with Self-Attention
He et al., 2017 + ELMo	84.6	Deep contextualized word representations
Li et al.	86.0	Dependency or Span, End-to-End Uniform Semantic Role Labeling
SRL-MM + XLNet	87.67	Syntax-driven Approach for Semantic Role Labeling
CRF2o + RoBERTa	88.32	Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
He et al.	81.7	Deep Semantic Role Labeling: What Works and What's Next	-

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