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Chinese Spelling Check (CSC) is a task to detect and correct spelling errorsin Chinese natural language. Existing methods have made attempts to incorporatethe similarity knowledge between Chinese characters. However, they take thesimilarity knowledge as either an external input resource or just heuristicrules. This paper proposes to incorporate phonological and visual similarityknowledge into language models for CSC via a specialized graph convolutionalnetwork (SpellGCN). The model builds a graph over the characters, and SpellGCNis learned to map this graph into a set of inter-dependent characterclassifiers. These classifiers are applied to the representations extracted byanother network, such as BERT, enabling the whole network to be end-to-endtrainable. Experiments (The dataset and all code for this paper are availableat https://github.com/ACL2020SpellGCN/SpellGCN) are conducted on threehuman-annotated datasets. Our method achieves superior performance againstprevious models by a large margin.

SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check