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 …

Amid the rapid advancement of neural machine translation, the challenge of data sparsity
has been a major obstacle. To address this issue, this study proposes a general data …

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 …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

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 …

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 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 …