SOTAVerified

Linguistic Acceptability

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

Image Source: Warstadt et al

Papers

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Showing 1–50 of 72 papers

TitleStatusHype
Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective—0
Fietje: An open, efficient LLM for DutchCode2
Robust ASR Error Correction with Conservative Data Filtering—0
Learning Phonotactics from Linguistic Informants—0
MELA: Multilingual Evaluation of Linguistic AcceptabilityCode0
Not all layers are equally as important: Every Layer Counts BERT—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
How well can machine-generated texts be identified and can language models be trained to avoid identification?—0
JCoLA: Japanese Corpus of Linguistic AcceptabilityCode1
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
A Neural-Symbolic Approach Towards Identifying Grammatically Correct Sentences—0
NoCoLA: The Norwegian Corpus of Linguistic AcceptabilityCode0
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language ModelsCode0
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-TuningCode1
Revisiting Acceptability JudgementsCode0
Can BERT eat RuCoLA? Topological Data Analysis to ExplainCode0
ScandEval: A Benchmark for Scandinavian Natural Language ProcessingCode1
ChatGPT: Jack of all trades, master of noneCode1
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and EvaluationCode1
RuCoLA: Russian Corpus of Linguistic AcceptabilityCode1
LLM.int8(): 8-bit Matrix Multiplication for Transformers at ScaleCode5
Acceptability Judgements via Examining the Topology of Attention MapsCode0
VALUE: Understanding Dialect Disparity in NLUCode0
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
Cross-Architecture Distillation Using Bidirectional CMOW Embeddings—0
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpusCode0
Revisiting the Uniform Information Density Hypothesis—0
General Cross-Architecture Distillation of Pretrained Language Models into Matrix EmbeddingsCode0
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning—0
Charformer: Fast Character Transformers via Gradient-based Subword TokenizationCode1
Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT—0
Language Models Use Monotonicity to Assess NPI LicensingCode0
DaLAJ - a dataset for linguistic acceptability judgments for Swedish: Format, baseline, sharing—0
FNet: Mixing Tokens with Fourier TransformsCode1
DaLAJ – a dataset for linguistic acceptability judgments for Swedish—0
Entailment as Few-Shot LearnerCode1
How to Train BERT with an Academic BudgetCode1
CLEAR: Contrastive Learning for Sentence Representation—0
RealFormer: Transformer Likes Residual AttentionCode1
Grammaticality and Language Modelling—0
A Statistical Framework for Low-bitwidth Training of Deep Neural NetworksCode1
Domain Adversarial Fine-Tuning as an Effective RegularizerCode0
GeDi: Generative Discriminator Guided Sequence GenerationCode1
Big Bird: Transformers for Longer SequencesCode1
Towards Debiasing Sentence RepresentationsCode1
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?Code0
DeBERTa: Decoding-enhanced BERT with Disentangled AttentionCode2
On the Robustness of Language Encoders against Grammatical ErrorsCode1
Synthesizer: Rethinking Self-Attention in Transformer ModelsCode1
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All datasetsCoLARuCoLACoLA DevItaCoLADaLAJ

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1En-BERT + TDA + PCAAccuracy88.6—Unverified
2BERT+TDAAccuracy88.2—Unverified
3RoBERTa+TDAAccuracy87.3—Unverified
4deberta-v3-base+tasksourceAccuracy87.15—Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy86.4—Unverified
6LTG-BERT-base 98MAccuracy82.7—Unverified
7ELC-BERT-base 98MAccuracy82.6—Unverified
8En-BERT + TDAAccuracy82.1—Unverified
9FNet-LargeAccuracy78—Unverified
10LTG-BERT-small 24MAccuracy77.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Ru-RoBERTa+TDAMCC0.59—Unverified
2ruRoBERTaMCC0.53—Unverified
3Ru-BERT+TDAMCC0.48—Unverified
4RemBERTMCC0.44—Unverified
5ruBERTMCC0.42—Unverified
6ruGPT-3MCC0.3—Unverified
7ruT5MCC0.25—Unverified
8mBERTMCC0.15—Unverified
9XLM-RMCC0.13—Unverified
#ModelMetricClaimedVerifiedStatus
1En-BERT + TDAAccuracy88.6—Unverified
2XLM-R (pre-trained) + TDAAccuracy73—Unverified
3DeBERTa (large)Accuracy69.5—Unverified
4TinyBERT-6 67MAccuracy54—Unverified
5Synthesizer (R+V)Accuracy53.3—Unverified
6En-BERT (pre-trained) + TDAMCC0.42—Unverified
#ModelMetricClaimedVerifiedStatus
1XLM-R + TDAMCC0.68—Unverified
2XLM-RMCC0.52—Unverified
3It-BERT (pre-trained) + TDAMCC0.48—Unverified
4mBERTMCC0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Sw-BERT + H0MAccuracy76.9—Unverified