Decopy: Detect and correct with Pinyin for Chinese spelling correction

Chinese Spelling Correction (CSC) is a crucial task. Previous studies have been affected by issues such as misleading error information, over-reliance on high-frequency characters, and scarcity of training data. This paper proposes a new CSC model, named Decopy, which employs an advanced detection-correction framework and a novel error masking strategy with pinyin features. Decopy can not only capture semantic information (word embeddings) and positional information (position embeddings) of words, but also recognize the phonetic features (pinyin embeddings) of words. It can start directly from the phonetics of words, connect similarities at the pinyin level, and make the most of useful phonetic information, thereby reducing reliance on confusions and minimizing misleading information. Additionally, to address the scarcity of training data, we have constructed a new CSC dataset based on THUCNews and used it for pre-training Decopy. This enables Decopy to have a more comprehensive understanding of the input information, especially the additional pinyin information. Experiments on SIGHAN15 and three domain-specific datasets, namely LAW, Medical (Med), and Official Document Writing (Odw), show that Decopy achieves significant improvements and outperforms the previous state-of-the-art methods. Finally, we tested and analyzed several high-performance LLMs on the CSC task, and fine-tuned ChatGLM3-6B for the CSC task to further evaluate the capabilities of LLMs in this field.