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 …

We propose LaserTagger-a sequence tagging approach that casts text generation as a text
editing task. Target texts are reconstructed from the inputs using three main edit operations …

Until now, error type performance for Grammatical Error Correction (GEC) systems could
only be measured in terms of recall because system output is not annotated. To overcome …

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

The CoNLL-2014 shared task was devoted to grammatical error correction of all error types.
In this paper, we give the task definition, present the data sets, and describe the evaluation …

This paper presents the first study using neural machine translation (NMT) for grammatical
error correction (GEC). We propose a twostep approach to handle the rare word problem in …

Grammar error correction (GEC) is an important application aspect of natural language
processing techniques. The past decade has witnessed significant progress achieved in …

Following great success in the image processing field, the idea of adversarial training has
been applied to tasks in the natural language processing (NLP) field. One promising …

Most of the neural sequence-to-sequence (seq2seq) models for grammatical error correction
(GEC) have two limitations:(1) a seq2seq model may not be well generalized with only …

Natural language correction has the potential to help language learners improve their
writing skills. While approaches with separate classifiers for different error types have high …