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 …

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

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

GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of Natural
Language Processing tasks. However, there is a relative lack of detailed published analysis …

This work proposes a syntax-enhanced grammatical error correction (GEC) approach
named SynGEC that effectively incorporates dependency syntactic information into the …

We propose Seq2Edits, an open-vocabulary approach to sequence editing for natural
language processing (NLP) tasks with a high degree of overlap between input and output …

Detecting and fixing bugs are two of the most important yet frustrating parts of the software
development cycle. Existing bug detection tools are based mainly on static analyzers, which …

The main barrier to progress in the task of Formality Style Transfer is the inadequacy of
training data. In this paper, we study how to augment parallel data and propose novel and …