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Linguistic Acceptability

Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical.

Image Source: Warstadt et al

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 61–70 of 72 papers

Title	Date	Tasks	Status	Hype
ERNIE: Enhanced Language Representation with Informative Entities	May 17, 2019	Entity LinkingEntity Typing	CodeCode Available	0
MELA: Multilingual Evaluation of Linguistic Acceptability	Nov 15, 2023	Code GenerationCross-Lingual Transfer	CodeCode Available	0
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus	Sep 24, 2021	CoLAdomain classification	CodeCode Available	0
Multi-Task Deep Neural Networks for Natural Language Understanding	Jan 31, 2019	Domain AdaptationLanguage Modeling	CodeCode Available	0
Natural Language Generation for Effective Knowledge Distillation	Nov 1, 2019	Knowledge DistillationLinguistic Acceptability	CodeCode Available	0
Neural Network Acceptability Judgments	May 31, 2018	CoLAGeneral Classification	CodeCode Available	0
General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings	Sep 17, 2021	CoLALinguistic Acceptability	CodeCode Available	0
NoCoLA: The Norwegian Corpus of Linguistic Acceptability	Jun 13, 2023	Binary ClassificationDiagnostic	CodeCode Available	0
TinyBERT: Distilling BERT for Natural Language Understanding	Sep 23, 2019	Knowledge DistillationLanguage Modelling	CodeCode Available	0
SpanBERT: Improving Pre-training by Representing and Predicting Spans	Jul 24, 2019	Coreference ResolutionLinguistic Acceptability	CodeCode Available	0

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All datasets CoLA RuCoLA CoLA Dev ItaCoLA DaLAJ

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	En-BERT + TDA + PCA	Accuracy	88.6	—	Unverified
2	BERT+TDA	Accuracy	88.2	—	Unverified
3	RoBERTa+TDA	Accuracy	87.3	—	Unverified
4	deberta-v3-base+tasksource	Accuracy	87.15	—	Unverified
5	RoBERTa-large 355M + Entailment as Few-shot Learner	Accuracy	86.4	—	Unverified
6	LTG-BERT-base 98M	Accuracy	82.7	—	Unverified
7	ELC-BERT-base 98M	Accuracy	82.6	—	Unverified
8	En-BERT + TDA	Accuracy	82.1	—	Unverified
9	FNet-Large	Accuracy	78	—	Unverified
10	LTG-BERT-small 24M	Accuracy	77.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ru-RoBERTa+TDA	MCC	0.59	—	Unverified
2	ruRoBERTa	MCC	0.53	—	Unverified
3	Ru-BERT+TDA	MCC	0.48	—	Unverified
4	RemBERT	MCC	0.44	—	Unverified
5	ruBERT	MCC	0.42	—	Unverified
6	ruGPT-3	MCC	0.3	—	Unverified
7	ruT5	MCC	0.25	—	Unverified
8	mBERT	MCC	0.15	—	Unverified
9	XLM-R	MCC	0.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	En-BERT + TDA	Accuracy	88.6	—	Unverified
2	XLM-R (pre-trained) + TDA	Accuracy	73	—	Unverified
3	DeBERTa (large)	Accuracy	69.5	—	Unverified
4	TinyBERT-6 67M	Accuracy	54	—	Unverified
5	Synthesizer (R+V)	Accuracy	53.3	—	Unverified
6	En-BERT (pre-trained) + TDA	MCC	0.42	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XLM-R + TDA	MCC	0.68	—	Unverified
2	XLM-R	MCC	0.52	—	Unverified
3	It-BERT (pre-trained) + TDA	MCC	0.48	—	Unverified
4	mBERT	MCC	0.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Sw-BERT + H0M	Accuracy	76.9	—	Unverified