# Mining Error Templates for Grammatical Error Correction

arXiv: https://arxiv.org/abs/2206.11569 · HTML: https://mlsanity.com/paper/2206.11569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](https://mlsanity.com/paper/1804.05940/) (2018)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [Neural Network Translation Models for Grammatical Error Correction](https://mlsanity.com/paper/1606.00189/) (2016)

## Built on by (most cited first, 1 of 1)

- [A BERT-based Unsupervised Grammatical Error Correction Framework](https://mlsanity.com/paper/2303.17367/) (2023)

## Abstract (arXiv)

Some grammatical error correction (GEC) systems incorporate hand-crafted rules and achieve positive results. However, manually defining rules is time-consuming and laborious. In view of this, we propose a method to mine error templates for GEC automatically. An error template is a regular expression aiming at identifying text errors. We use the web crawler to acquire such error templates from the Internet. For each template, we further select the corresponding corrective action by using the language model perplexity as a criterion. We have accumulated 1,119 error templates for Chinese GEC based on this method. Experimental results on the newly proposed CTC-2021 Chinese GEC benchmark show that combing our error templates can effectively improve the performance of a strong GEC system, especially on two error types with very little training data. Our error templates are available at urlhttps://github.com/HillZhang1999/gecerrortemplate.