  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}.

Computation and Language