SOTAVerified

Linguistic Acceptability

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

Image Source: Warstadt et al

Papers

Showing 5172 of 72 papers

TitleStatusHype
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE0
A Neural-Symbolic Approach Towards Identifying Grammatically Correct Sentences0
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning0
Revisiting the Uniform Information Density Hypothesis0
Can BERT eat RuCoLA? Topological Data Analysis to ExplainCode0
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language ModelsCode0
Language Models Use Monotonicity to Assess NPI LicensingCode0
Domain Adversarial Fine-Tuning as an Effective RegularizerCode0
VALUE: Understanding Dialect Disparity in NLUCode0
Acceptability Judgements via Examining the Topology of Attention MapsCode0
ERNIE: Enhanced Language Representation with Informative EntitiesCode0
MELA: Multilingual Evaluation of Linguistic AcceptabilityCode0
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpusCode0
Multi-Task Deep Neural Networks for Natural Language UnderstandingCode0
Natural Language Generation for Effective Knowledge DistillationCode0
Neural Network Acceptability JudgmentsCode0
General Cross-Architecture Distillation of Pretrained Language Models into Matrix EmbeddingsCode0
NoCoLA: The Norwegian Corpus of Linguistic AcceptabilityCode0
TinyBERT: Distilling BERT for Natural Language UnderstandingCode0
SpanBERT: Improving Pre-training by Representing and Predicting SpansCode0
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?Code0
Revisiting Acceptability JudgementsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1En-BERT + TDA + PCAAccuracy88.6Unverified
2BERT+TDAAccuracy88.2Unverified
3RoBERTa+TDAAccuracy87.3Unverified
4deberta-v3-base+tasksourceAccuracy87.15Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy86.4Unverified
6LTG-BERT-base 98MAccuracy82.7Unverified
7ELC-BERT-base 98MAccuracy82.6Unverified
8En-BERT + TDAAccuracy82.1Unverified
9FNet-LargeAccuracy78Unverified
10LTG-BERT-small 24MAccuracy77.6Unverified
#ModelMetricClaimedVerifiedStatus
1Ru-RoBERTa+TDAMCC0.59Unverified
2ruRoBERTaMCC0.53Unverified
3Ru-BERT+TDAMCC0.48Unverified
4RemBERTMCC0.44Unverified
5ruBERTMCC0.42Unverified
6ruGPT-3MCC0.3Unverified
7ruT5MCC0.25Unverified
8mBERTMCC0.15Unverified
9XLM-RMCC0.13Unverified
#ModelMetricClaimedVerifiedStatus
1En-BERT + TDAAccuracy88.6Unverified
2XLM-R (pre-trained) + TDAAccuracy73Unverified
3DeBERTa (large)Accuracy69.5Unverified
4TinyBERT-6 67MAccuracy54Unverified
5Synthesizer (R+V)Accuracy53.3Unverified
6En-BERT (pre-trained) + TDAMCC0.42Unverified
#ModelMetricClaimedVerifiedStatus
1XLM-R + TDAMCC0.68Unverified
2XLM-RMCC0.52Unverified
3It-BERT (pre-trained) + TDAMCC0.48Unverified
4mBERTMCC0.36Unverified
#ModelMetricClaimedVerifiedStatus
1Sw-BERT + H0MAccuracy76.9Unverified