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Semantic Role Labeling On Conll 2005

Metrics

F1

Results

Performance results of various models on this benchmark

Model Name
F1
Paper TitleRepository
Li et al. (2019) + ELMo86.3Dependency or Span, End-to-End Uniform Semantic Role Labeling-
CRF2o + RoBERTa89.54Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments-
He et al. (2018) + ELMo86.0Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling-
CRF2o87.87Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments-
Li et al. (2019)83.0Dependency or Span, End-to-End Uniform Semantic Role Labeling-
BiLSTM-Span (Ensemble, predicates given)88.5A Span Selection Model for Semantic Role Labeling-
SRL-MM + XLNet89.80Syntax-driven Approach for Semantic Role Labeling
Li et al. (2019) (Ensemble)87.7Dependency or Span, End-to-End Uniform Semantic Role Labeling-
HeSyFu89.04--
MRC-SRL90.0An MRC Framework for Semantic Role Labeling-
BiLSTM-Span87.6A Span Selection Model for Semantic Role Labeling-
He et al. (2018)82.5Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling-
LISA86.04Linguistically-Informed Self-Attention for Semantic Role Labeling-
Mohammadshahi and Henderson (2021)88.93Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling-
CRF2o + BERT89.03Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments-
0 of 15 row(s) selected.
Semantic Role Labeling On Conll 2005 | SOTA | HyperAI