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With the development of pre-trained models and the incorporation of phoneticand graphic information, neural models have achieved high scores in ChineseSpelling Check (CSC). However, it does not provide a comprehensive reflectionof the models' capability due to the limited test sets. In this study, weabstract the representative model paradigm, implement it with nine structuresand experiment them on comprehensive test sets we constructed with differentpurposes. We perform a detailed analysis of the results and find that: 1)Fusing phonetic and graphic information reasonably is effective for CSC. 2)Models are sensitive to the error distribution of the test set, which reflectsthe shortcomings of models and reveals the direction we should work on. 3)Whether or not the errors and contexts have been seen has a significant impacton models. 4) The commonly used benchmark, SIGHAN, can not reliably evaluatemodels' performance.

A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check