General and Domain-adaptive Chinese Spelling Check with Error-consistent Pretraining

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Abstract

The lack of label data is one of the significant bottlenecks for Chinese Spelling Check. Existing researches use the automatic generation method by exploiting unlabeled data to expand the supervised corpus. However, there is a big gap between the real input scenario and automatically generated corpus. Thus, we develop a competitive general speller ECSpell, which adopts the Error-consistent masking strategy to create data for pretraining. This error-consistency masking strategy is used to specify the error types of automatically generated sentences consistent with the real scene. The experimental result indicates that our model outperforms previous state-of-the-art models on the general benchmark. Moreover, spellers often work within a particular domain in real life. Due to many uncommon domain terms, experiments on our built domain-specific datasets show that general models perform terribly. Inspired by the common practice of input methods, we propose to add an alterable user dictionary to handle the zero-shot domain-adaption problem. Specifically, we attach a User Dictionary guided inference module (UD) to a general token classification-based speller. Our experiments demonstrate that ECSpell UD , namely, ECSpell combined with UD, surpasses all the other baselines broadly, even approaching the performance on the general benchmark. 1

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