  Chinese Spelling Check (CSC) aims to detect and correct error tokens in
Chinese contexts, which has a wide range of applications. However, it is
confronted with the challenges of insufficient annotated data and the issue
that previous methods may actually not fully leverage the existing datasets. In
this paper, we introduce our plug-and-play retrieval method with error-robust
information for Chinese Spelling Check (RERIC), which can be directly applied
to existing CSC models. The datastore for retrieval is built completely based
on the training data, with elaborate designs according to the characteristics
of CSC. Specifically, we employ multimodal representations that fuse phonetic,
morphologic, and contextual information in the calculation of query and key
during retrieval to enhance robustness against potential errors. Furthermore,
in order to better judge the retrieved candidates, the n-gram surrounding the
token to be checked is regarded as the value and utilized for specific
reranking. The experiment results on the SIGHAN benchmarks demonstrate that our
proposed method achieves substantial improvements over existing work.
