Grammatical error correction (GEC) can be divided into sequence-to-edit (Seq2Edit) and
sequence-to-sequence (Seq2Seq) frameworks, both of which have their pros and cons. To …

Metrics are the foundation for automatic evaluation in grammatical error correction (GEC),
with their evaluation of the metrics (meta-evaluation) relying on their correlation with human …

Grammatical error correction (GEC) is a promising task aimed at correcting errors in a text.
Many methods have been proposed to facilitate this task with remarkable results. However …

The impression is crucial for the referring physicians to grasp key information since it is
concluded from the findings and reasoning of radiologists. To alleviate the workload of …

Nowadays, data augmentation through synthetic data has been widely used in the field of
Grammatical Error Correction (GEC) to alleviate the problem of data scarcity. However …

Abstract Sequence-to-sequence (seq2seq) models achieve comparable or better
grammatical error correction performance compared to sequence-to-edit (seq2edit) models …

While large-scale language models (LLMs) have demonstrated remarkable capabilities in
specific natural language processing (NLP) tasks, they may still lack proficiency compared to …

Current mainstream for Chinese grammatical error correction methods rely on deep neural
network models, which require a large amount of high-quality data for training. However …

Evaluating the performance of Grammatical Error Correction (GEC) models has become
increasingly challenging, as large language model (LLM)-based GEC systems often …

This study explores enhancing grammatical error correction (GEC) through artificial error
generation (AEG) using language models (LMs). Specifically, we fine-tune Llama 2-based …