# Chinese Grammatical Correction Using BERT-based Pre-trained Model

arXiv: https://arxiv.org/abs/2011.02093 · HTML: https://mlsanity.com/paper/2011.02093/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2106.01609/) (2021)
- [A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model](https://mlsanity.com/paper/2201.10707/) (2022)
- [uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers](https://mlsanity.com/paper/2209.07068/) (2022)
- [An Alignment-Agnostic Model for Chinese Text Error Correction](https://mlsanity.com/paper/2104.07190/) (2021)
- [Pretraining Chinese BERT for Detecting Word Insertion and Deletion Errors](https://mlsanity.com/paper/2204.12052/) (2022)
- [Tgea: An error-annotated dataset and benchmark tasks for text generation from pretrained language models](https://mlsanity.com/paper/2503.04232/) (2025)

## Abstract (arXiv)

In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization. In this study, we verify the effectiveness of two methods that incorporate a BERT-based pre-trained model developed by Cui et al. (2020) into an encoder-decoder model on Chinese grammatical error correction tasks. We also analyze the error type and conclude that sentence-level errors are yet to be addressed.