Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors.
A non-negligible shortcoming of the pre-defined context patterns is that they cannot be flexibly generalized to all kinds of semantic classes, and we call this phenomenon as "semantic sensitivity".
In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities.
However, there exists a gap between the learned knowledge of PLMs and the goal of CSC task.
Learning an empirically effective model with generalization using limited data is a challenging task for deep neural networks.