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 41–50 of 620 papers

Title	Date	Tasks	Status	Hype	Score
A Graph-Based Neural Model for End-to-End Frame Semantic Parsing	Sep 25, 2021	graph constructionSemantic Parsing	CodeCode Available	1	5
BERTje: A Dutch BERT Model	Dec 19, 2019	Language ModelingLanguage Modelling	CodeCode Available	1	5
Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach	Dec 1, 2020	Semantic Role Labeling	CodeCode Available	1	5
Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling	Dec 5, 2022	ClassificationMulti-class Classification	CodeCode Available	1	5
Semantic Role Labeling as Syntactic Dependency Parsing	Oct 21, 2020	Dependency ParsingSemantic Role Labeling	CodeCode Available	1	5
Demonyms and Compound Relational Nouns in Nominal Open IE	Jun 1, 2016	Open Information ExtractionSemantic Role Labeling	CodeCode Available	0	5
Dependency or Span, End-to-End Uniform Semantic Role Labeling	Jan 16, 2019	Model OptimizationSemantic Role Labeling	CodeCode Available	0	5
Deep Semantic Role Labeling with Self-Attention	Dec 5, 2017	GPUNatural Language Understanding	CodeCode Available	0	5
Deep Semantic Role Labeling: What Works and What's Next	Jul 1, 2017	Predicate DetectionSemantic Role Labeling	CodeCode Available	0	5
Dict2vec : Learning Word Embeddings using Lexical Dictionaries	Sep 1, 2017	General ClassificationKnowledge Graphs	CodeCode Available	0	5

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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