Chinese Spelling Check (CSC) refers to the detection and correction of
spelling errors in Chinese texts. In practical application scenarios, it is
important to make CSC models have the ability to correct errors across
different domains. In this paper, we propose a retrieval-augmented spelling
check framework called RSpell, which searches corresponding domain terms and
incorporates them into CSC models. Specifically, we employ pinyin fuzzy
matching to search for terms, which are combined with the input and fed into
the CSC model. Then, we introduce an adaptive process control mechanism to
dynamically adjust the impact of external knowledge on the model. Additionally,
we develop an iterative strategy for the RSpell framework to enhance reasoning
capabilities. We conducted experiments on CSC datasets in three domains: law,
medicine, and official document writing. The results demonstrate that RSpell
achieves state-of-the-art performance in both zero-shot and fine-tuning
scenarios, demonstrating the effectiveness of the retrieval-augmented CSC
framework. Our code is available at this https URL

我们提出了一种称为 RSpell 的检索增强的拼写检查框架，通过搜索相应的领域术语并将其合并到拼写检查模型中，来增强其错误纠正能力。通过结合拼音模糊匹配和输入，我们引入了一种自适应过程控制机制来动态调整外部知识对模型的影响，同时采用迭代策略加强了 RSpell 框架的推理能力。通过在法律、医学和官方文件撰写三个领域的 CSC 数据集上进行实验，结果表明 RSpell 在零样本和微调场景下均取得了最先进的性能，验证了检索增强的拼写检查框架的有效性。我们的代码可在 https URL 获取。