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 321–330 of 620 papers

Title	Date	Tasks	Status
Friend-training: Learning from Models of Different but Related Tasks	Jan 31, 2023	Dialogue RewritingDialogue Understanding	—Unverified
From Natural Language Specifications to Program Input Parsers	Aug 1, 2013	Dependency ParsingSemantic Role Labeling	—Unverified
From Textual Information Sources to Linked Data in the Agatha Project	Sep 3, 2019	named-entity-recognitionNamed Entity Recognition	—Unverified
Generating Adequate Distractors for Multiple-Choice Questions	Oct 23, 2020	FormMultiple-choice	—Unverified
Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling	Jul 1, 2015	Question AnsweringSemantic Role Labeling	—Unverified
Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources	Jan 1, 2016	General ClassificationMachine Translation	—Unverified
German and French Neural Supertagging Experiments for LTAG Parsing	Jul 1, 2018	Dependency ParsingSemantic Parsing	—Unverified
Getting the Roles Right: Using FrameNet in NLP	May 1, 2015	Semantic Role Labelingtext annotation	—Unverified
Going beyond sentences when applying tree kernels	Jun 1, 2014	Machine TranslationNamed Entity Recognition (NER)	—Unverified
Good Automatic Authentication Question Generation	Sep 1, 2016	Common Sense ReasoningDependency Parsing	—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