RSpell：面向领域自适应中文拼写检查的检索增强框架
RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check
AI总结：
提出检索增强框架RSpell，通过拼音模糊匹配检索领域术语并自适应控制外部知识，在零样本和微调场景下于法律、医学、公文三个领域数据集上达到最优性能。
摘要：
中文拼写检查（CSC）是指检测和纠正中文文本中拼写错误的任务。在实际应用场景中，使CSC模型具备跨领域纠错能力至关重要。本文提出了一种名为RSpell的检索增强拼写检查框架，该框架搜索相应的领域术语并将其融入CSC模型。具体而言，我们采用拼音模糊匹配来搜索术语，将搜索到的术语与输入结合后馈入CSC模型。随后，我们引入自适应过程控制机制，动态调整外部知识对模型的影响。此外，我们为RSpell框架开发了一种迭代策略以增强推理能力。我们在法律、医学和公文写作三个领域的CSC数据集上进行了实验。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，证明了检索增强CSC框架的有效性。我们的代码可在https://github.com/47777777/Rspell获取。
英文摘要：
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 https://github.com/47777777/Rspell.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
