  While pre-trained Chinese language models have demonstrated impressive
performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task
remains a challenge. Previous research has explored using information such as
glyphs and phonetics to improve the ability to distinguish misspelled
characters, with good results. However, the generalization ability of these
models is not well understood: it is unclear whether they incorporate
glyph-phonetic information and, if so, whether this information is fully
utilized. In this paper, we aim to better understand the role of glyph-phonetic
information in the CSC task and suggest directions for improvement.
Additionally, we propose a new, more challenging, and practical setting for
testing the generalizability of CSC models. All code is made publicly
available.
