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 101–110 of 620 papers

Title	Date	Tasks	Status	Hype
Comparing Span Extraction Methods for Semantic Role Labeling	Aug 1, 2021	Semantic Role Labeling	—Unverified	0
Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling	Aug 1, 2021	Semantic Role LabelingSemantic Role Labeling (predicted predicates)	CodeCode Available	1
Argument Linking: A Survey and Forecast	Jul 18, 2021	Natural Language UnderstandingSemantic Role Labeling	—Unverified	0
Constraining Linear-chain CRFs to Regular Languages	Jun 14, 2021	Semantic Role LabelingStructured Prediction	CodeCode Available	0
Predicate Representations and Polysemy in VerbNet Semantic Parsing	Jun 1, 2021	Semantic ParsingSemantic Role Labeling	CodeCode Available	0
Tuning Deep Active Learning for Semantic Role Labeling	Jun 1, 2021	Active LearningSemantic Role Labeling	—Unverified	0
Deep Learning on Graphs for Natural Language Processing	Jun 1, 2021	Deep Learninggraph construction	—Unverified	0
Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources	Jun 1, 2021	Semantic Role LabelingSentence	CodeCode Available	1
Encoder-decoder based unified semantic role labeling with label-aware syntax	May 18, 2021	DecoderSemantic Role Labeling	CodeCode Available	0
Neural Unsupervised Semantic Role Labeling	Apr 19, 2021	ClusteringSemantic Role Labeling	—Unverified	0

Show:10 25 50

← PrevPage 11 of 62Next →

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