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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 41–50 of 72 papers

Title	Date	Tasks	Status	Hype
Grammaticality and Language Modelling	Nov 1, 2020	CoLAExperimental Design	—Unverified	0
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks	Oct 27, 2020	Linguistic AcceptabilityNatural Language Inference	CodeCode Available	1
Domain Adversarial Fine-Tuning as an Effective Regularizer	Sep 28, 2020	Linguistic AcceptabilityNatural Language Understanding	CodeCode Available	0
GeDi: Generative Discriminator Guided Sequence Generation	Sep 14, 2020	AttributeLinguistic Acceptability	CodeCode Available	1
Big Bird: Transformers for Longer Sequences	Jul 28, 2020	Linguistic AcceptabilityNatural Language Inference	CodeCode Available	1
Towards Debiasing Sentence Representations	Jul 16, 2020	Linguistic AcceptabilityNatural Language Understanding	CodeCode Available	1
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?	Jun 19, 2020	Linguistic AcceptabilityNatural Language Inference	CodeCode Available	0
DeBERTa: Decoding-enhanced BERT with Disentangled Attention	Jun 5, 2020	Common Sense ReasoningCoreference Resolution	CodeCode Available	2
On the Robustness of Language Encoders against Grammatical Errors	May 12, 2020	Cloze TestLinguistic Acceptability	CodeCode Available	1
Synthesizer: Rethinking Self-Attention in Transformer Models	May 2, 2020	Abstractive Text SummarizationDialogue Generation	CodeCode Available	1

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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