  Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling
errors. Recently, related researches focus on introducing character similarity
from confusion set to enhance the CSC models, ignoring the context of
characters that contain richer information. To make better use of contextual
information, we propose a simple yet effective Curriculum Learning (CL)
framework for the CSC task. With the help of our model-agnostic CL framework,
existing CSC models will be trained from easy to difficult as humans learn
Chinese characters and achieve further performance improvements. Extensive
experiments and detailed analyses on widely used SIGHAN datasets show that our
method outperforms previous state-of-the-art methods. More instructively, our
study empirically suggests that contextual similarity is more valuable than
character similarity for the CSC task.
