Mining Error Templates for Grammatical Error Correction

TL;DR

This paper introduces an automatic method to mine error templates from the internet for Chinese grammatical error correction, enhancing system performance especially on low-resource error types.

Contribution

It proposes a novel approach to automatically extract and utilize error templates for GEC, reducing manual rule creation and improving correction accuracy.

Findings

Improved GEC performance on low-resource error types

Successfully mined 1,119 error templates for Chinese GEC

Templates enhance a strong GEC system's effectiveness

Abstract

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 \url{https://github.com/HillZhang1999/gec_error_template}.