Linguistic Acceptability
47 papers with code • 5 benchmarks • 5 datasets
Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical.
Image Source: Warstadt et al
Benchmarks
These leaderboards are used to track progress in Linguistic Acceptability
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Libraries
Use these libraries to find Linguistic Acceptability models and implementations
GLUE
CoLA
ItaCoLA
RuCoLA
Latest papers
How to Train BERT with an Academic Budget
peteriz/academic-budget-bert
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EMNLP 2021
While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford.
RealFormer: Transformer Likes Residual Attention
google-research/google-research
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Findings (ACL) 2021
Transformer is the backbone of modern NLP models.
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
cjf00000/StatQuant
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NeurIPS 2020
We show that the FQT gradient is an unbiased estimator of the QAT gradient, and we discuss the impact of gradient quantization on its variance.
Domain Adversarial Fine-Tuning as an Effective Regularizer
GeorgeVern/AFTERV1.0
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Findings of the Association for Computational Linguistics 2020
To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer.
GeDi: Generative Discriminator Guided Sequence Generation
salesforce/GeDi
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Findings (EMNLP) 2021
While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate.
Big Bird: Transformers for Longer Sequences
huggingface/transformers
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NeurIPS 2020
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Towards Debiasing Sentence Representations
pliang279/sent_debias
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ACL 2020
As natural language processing methods are increasingly deployed in real-world scenarios such as healthcare, legal systems, and social science, it becomes necessary to recognize the role they potentially play in shaping social biases and stereotypes.
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
huggingface/transformers
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EMNLP (sustainlp) 2020
Humans read and write hundreds of billions of messages every day.
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
huggingface/transformers
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ICLR 2021
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
On the Robustness of Language Encoders against Grammatical Errors
uclanlp/ProbeGrammarRobustness
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ACL 2020
We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors.
