  Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling
errors, which are mainly caused by the phonological or visual similarity.
Recently, pre-trained language models (PLMs) promote the progress of CSC task.
However, there exists a gap between the learned knowledge of PLMs and the goal
of CSC task. PLMs focus on the semantics in text and tend to correct the
erroneous characters to semantically proper or commonly used ones, but these
aren't the ground-truth corrections. To address this issue, we propose an
Error-driven COntrastive Probability Optimization (ECOPO) framework for CSC
task. ECOPO refines the knowledge representations of PLMs, and guides the model
to avoid predicting these common characters through an error-driven way.
Particularly, ECOPO is model-agnostic and it can be combined with existing CSC
methods to achieve better performance. Extensive experiments and detailed
analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective.
