Semantic Role Labeling On Ontonotes

Results

Performance results of various models on this benchmark

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

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