  Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling
errors. Recent researches start from the pretrained knowledge of language
models and take multimodal information into CSC models to improve the
performance. However, they overlook the rich knowledge in the dictionary, the
reference book where one can learn how one character should be pronounced,
written, and used. In this paper, we propose the LEAD framework, which renders
the CSC model to learn heterogeneous knowledge from the dictionary in terms of
phonetics, vision, and meaning. LEAD first constructs positive and negative
samples according to the knowledge of character phonetics, glyphs, and
definitions in the dictionary. Then a unified contrastive learning-based
training scheme is employed to refine the representations of the CSC models.
Extensive experiments and detailed analyses on the SIGHAN benchmark datasets
demonstrate the effectiveness of our proposed methods.
