Abstract Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of grammatical errors, such …

Grammatical error correction (GEC) is an important application aspect of natural language
processing techniques, and GEC system is a kind of very important intelligent system that …

In this paper, we present a simple and efficient GEC sequence tagger using a Transformer
encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first …

This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC).
As with the CoNLL-2014 shared task, participants are required to correct all types of errors in …

ChatGPT, a large-scale language model based on the advanced GPT-3.5 architecture, has
shown remarkable potential in various Natural Language Processing (NLP) tasks. However …

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

Considerable effort has been made to address the data sparsity problem in neural
grammatical error correction. In this work, we propose a simple and surprisingly effective …

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 …

This paper investigates how to effectively incorporate a pre-trained masked language model
(MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error …

The incorporation of pseudo data in the training of grammatical error correction models has
been one of the main factors in improving the performance of such models. However …