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 181–190 of 620 papers

Title	Date	Tasks	Status
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling	Sep 21, 2019	Semantic Role Labeling	CodeCode Available
Subword ELMo	Sep 18, 2019	Dependency ParsingNatural Language Inference	—Unverified
Reasoning Over Semantic-Level Graph for Fact Checking	Sep 9, 2019	Claim VerificationFact Checking	—Unverified
Semantic Role Labeling with Iterative Structure Refinement	Sep 7, 2019	Semantic Role LabelingSentence	CodeCode Available
Semantics-aware BERT for Language Understanding	Sep 5, 2019	Language ModelingLanguage Modelling	CodeCode Available
From Textual Information Sources to Linked Data in the Agatha Project	Sep 3, 2019	named-entity-recognitionNamed Entity Recognition	—Unverified
Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates	Sep 1, 2019	ClassificationGeneral Classification	—Unverified
ULSAna: Universal Language Semantic Analyzer	Sep 1, 2019	Semantic Role Labeling	—Unverified
Automatic Propbank Generation for Turkish	Sep 1, 2019	Information RetrievalMachine Translation	—Unverified
Dependency-Based Self-Attention for Transformer NMT	Sep 1, 2019	DecoderMachine Translation	—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