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

Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

google-research/bert NAACL 2019

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

RoBERTa: A Robustly Optimized BERT Pretraining Approach

pytorch/fairseq 26 Jul 2019

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

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

huggingface/transformers arXiv 2019

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).

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

google-research/ALBERT ICLR 2020

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

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.

FNet: Mixing Tokens with Fourier Transforms

google-research/google-research NAACL 2022

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).

Big Bird: Transformers for Longer Sequences

google-research/bigbird NeurIPS 2020

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

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

microsoft/DeBERTa ICLR 2021

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

Multi-Task Deep Neural Networks for Natural Language Understanding

namisan/mt-dnn ACL 2019

In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks.

data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

pytorch/fairseq Preprint 2022

While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind.