Semantic Role Labeling

Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". BIO notation is typically used for semantic role labeling.

Example:

| Housing | starts | are | expected | to | quicken | a | bit | from | August’s | pace | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | B-ARG1 | I-ARG1 | O | O | O | V | B-ARG2 | I-ARG2 | B-ARG3 | I-ARG3 | I-ARG3 |

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 411–420 of 620 papers

Title	Date	Tasks	Status
Structural Representations for Learning Relations between Pairs of Texts	Jul 1, 2015	Feature EngineeringNatural Language Inference	—Unverified
Semantic Role Labeling Improves Incremental Parsing	Jul 1, 2015	Information RetrievalMachine Translation	—Unverified
Joint Case Argument Identification for Japanese Predicate Argument Structure Analysis	Jul 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling	Jul 1, 2015	Question AnsweringSemantic Role Labeling	—Unverified
End-to-end learning of semantic role labeling using recurrent neural networks	Jul 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Frame-Semantic Role Labeling with Heterogeneous Annotations	Jul 1, 2015	Domain AdaptationSemantic Parsing	—Unverified
Transferring Coreference Resolvers with Posterior Regularization	Jul 1, 2015	Coreference ResolutionDependency Parsing	—Unverified
Open IE as an Intermediate Structure for Semantic Tasks	Jul 1, 2015	Open Information ExtractionReading Comprehension	—Unverified
Improving Chinese-English PropBank Alignment	Jun 1, 2015	Machine TranslationSemantic Role Labeling	—Unverified
Scaling Semantic Frame Annotation	Jun 1, 2015	Machine TranslationSemantic Role Labeling	—Unverified

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All datasets OntoNotes CoNLL 2005 CoNLL05 Brown CoNLL05 WSJ CoNLL12 CoNLL-2009 CoNLL-2012

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	HeSyFu	F1	88.59	—	Unverified
2	CRF2o + RoBERTa	F1	88.32	—	Unverified
3	MRC-SRL	F1	88.3	—	Unverified
4	ReCAT(pretrained on wikitext103)	F1	88	—	Unverified
5	SRL-MM + XLNet	F1	87.67	—	Unverified
6	CRF2o + BERT	F1	87.66	—	Unverified
7	RoBERTa+RegCCRF	F1	87.51	—	Unverified
8	RoBERTa+CRF	F1	87.27	—	Unverified
9	BiLSTM-Span (Ensemble)	F1	87	—	Unverified
10	BiLSTM-Span	F1	86.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MRC-SRL	F1	90	—	Unverified
2	SRL-MM + XLNet	F1	89.8	—	Unverified
3	CRF2o + RoBERTa	F1	89.54	—	Unverified
4	HeSyFu	F1	89.04	—	Unverified
5	CRF2o + BERT	F1	89.03	—	Unverified
6	Mohammadshahi and Henderson (2021)	F1	88.93	—	Unverified
7	BiLSTM-Span (Ensemble, predicates given)	F1	88.5	—	Unverified
8	CRF2o	F1	87.87	—	Unverified
9	Li et al. (2019) (Ensemble)	F1	87.7	—	Unverified
10	BiLSTM-Span	F1	87.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeepStruct multi-task w/ finetune	F1	92.1	—	Unverified
2	DeepStruct multi-task	F1	92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeepStruct multi-task	F1	95.5	—	Unverified
2	DeepStruct multi-task w/ finetune	F1	95.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeepStruct multi-task	F1	97.2	—	Unverified
2	DeepStruct multi-task w/ finetune	F1	96	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ours (High-Order model)	F1 (Arg.)	90.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HeSyFu	Avg. F1	88.59	—	Unverified