Grammatical Error Correction

85 papers with code • 11 benchmarks • 13 datasets

Grammatical Error Correction (GEC) is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors.

GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected to transform it to its corrected version. See the example given below:

Input (Erroneous) Output (Corrected)
She see Tom is catched by policeman in park at last night. She saw Tom caught by a policeman in the park last night.

Libraries

Use these libraries to find Grammatical Error Correction models and implementations
2 papers
7,355

Most implemented papers

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

zhawe01/fairseq-gec NAACL 2019

It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task.

A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction

nusnlp/mlconvgec2018 26 Jan 2018

We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network.

Neural Network Translation Models for Grammatical Error Correction

seaweiqing/image2story 1 Jun 2016

Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy.

The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction

todd-cook/ML-You-Can-Use WS 2019

Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits.

GECToR -- Grammatical Error Correction: Tag, Not Rewrite

grammarly/gector WS 2020

In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder.

LM-Critic: Language Models for Unsupervised Grammatical Error Correction

michiyasunaga/LM-Critic EMNLP 2021

Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive.

Mining Error Templates for Grammatical Error Correction

hillzhang1999/mucgec 23 Jun 2022

We have accumulated 1, 119 error templates for Chinese GEC based on this method.

Chinese grammatical error correction based on knowledge distillation

richard88888/chinese-noisy-text 31 Jul 2022

In view of the poor robustness of existing Chinese grammatical error correction models on attack test sets and large model parameters, this paper uses the method of knowledge distillation to compress model parameters and improve the anti-attack ability of the model.