Linguistic Acceptability

27 papers with code • 2 benchmarks • 2 datasets

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

Image Source: Warstadt et al

Datasets


Greatest papers with code

Big Bird: Transformers for Longer Sequences

tensorflow/models NeurIPS 2020

To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.

Linguistic Acceptability Natural Language Inference +3

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

tensorflow/models ICLR 2020

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.

Common Sense Reasoning Linguistic Acceptability +4

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

huggingface/transformers ICLR 2021

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.

Common Sense Reasoning Coreference Resolution +11

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

huggingface/transformers NeurIPS 2019

As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.

Hate Speech Detection Knowledge Distillation +8

RoBERTa: A Robustly Optimized BERT Pretraining Approach

huggingface/transformers 26 Jul 2019

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.

Common Sense Reasoning Language Modelling +6

FNet: Mixing Tokens with Fourier Transforms

labmlai/annotated_deep_learning_paper_implementations 9 May 2021

At longer input lengths, our FNet model is significantly faster: when compared to the "efficient" Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, while outpacing the fastest models across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs).

Linguistic Acceptability Machine Translation +5

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

PaddlePaddle/ERNIE 29 Jul 2019

Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.

Chinese Named Entity Recognition Chinese Reading Comprehension +9