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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