Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences. Recently, multi-domain CSC has gradually attracted the attention of researchers because it is more practicable. In this paper, we focus on the key flaw of the CSC model when adapting to multi-domain scenarios: the tendency to forget previously acquired knowledge upon learning new domain-specific knowledge (i.e., catastrophic forgetting). To address this, we propose a novel model-agnostic Multi-stage Knowledge Transfer (MKT) framework, which utilizes a continuously evolving teacher model for knowledge transfer in each domain, rather than focusing solely on new domain knowledge. It deserves to be mentioned that we are the first to apply continual learning methods to the multi-domain CSC task. Experiments prove the effectiveness of our proposed method, and further analyses demonstrate the importance of overcoming catastrophic forgetting for improving the model performance.

我们提出了一种创新的模型无关的多阶段知识传递框架，该框架通过在每个领域中利用不断演化的教师模型进行知识传递来解决多领域中文拼写纠正模型在适应时容易遗忘先前获得的知识的问题，实验证明了我们提出方法的有效性，并进一步分析表明克服灾难性遗忘对于提高模型性能的重要性。

多阶段知识迁移框架在多领域中文拼写纠错中的缓解灾难性遗忘