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
Probing for Predicate Argument Structures in Pretrained Language Models	May 1, 2022	Semantic Role Labeling	CodeCode Available
Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes	May 1, 2022	Semantic Role Labeling	—Unverified
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models	May 1, 2022	Semantic Role Labeling	CodeCode Available
Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance	May 1, 2022	Semantic Role Labeling	—Unverified
Zero-shot Cross-lingual Conversational Semantic Role Labeling	Apr 11, 2022	Response GenerationSemantic Role Labeling	CodeCode Available
Fast and Accurate Span-based Semantic Role Labeling as Graph Parsing	Jan 16, 2022	Semantic Role LabelingSentence	—Unverified
An MRC Framework for Semantic Role Labeling	Jan 16, 2022	Computational EfficiencyMachine Reading Comprehension	—Unverified
Toward Automatic Misinformation Detection Utilizing Fact-checked Information	Jan 16, 2022	Fact CheckingMisinformation	—Unverified
To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP	Nov 18, 2021	Dependency ParsingPart-Of-Speech Tagging	—Unverified
AMRize, then Parse! Enhancing AMR Parsing with PseudoAMR Data	Nov 16, 2021	Abstract Meaning RepresentationAMR 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