Grammatical Error Correction

114 papers with code • 11 benchmarks • 14 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.


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

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.

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.

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.

Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model

Katsumata420/generic-pretrained-GEC 24 May 2020

In this study, we explore the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC.

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.

Automatic Error Type Annotation for Arabic

camel-lab/arabic_error_type_annotation CoNLL (EMNLP) 2021

We present ARETA, an automatic error type annotation system for Modern Standard Arabic.

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction

hillzhang1999/mucgec NAACL 2022

This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.