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 …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Quality estimation models have been developed to assess the corrections made by
grammatical error correction (GEC) models when the reference or gold-standard corrections …

Deep neural networks and huge language models are becoming omnipresent in natural
language applications. As they are known for requiring large amounts of training data, there …

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 presents a simple recipe to train state-of-the-art multilingual Grammatical Error
Correction (GEC) models. We achieve this by first proposing a language-agnostic method to …

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 …

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 …

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 …