In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion. In this paper, we propose to decompose the CSC workflow into detection, reasoning, and searching subtasks so that the rich external knowledge about the Chinese language can be leveraged more directly and efficiently. Specifically, we design a plug-and-play detection-and-reasoning module that is compatible with existing SOTA non-autoregressive CSC models to further boost their performance. We find that the detection-and-reasoning module trained for one model can also benefit other models. We also study the primary interpretability provided by the task decomposition. Extensive experiments and detailed analyses demonstrate the effectiveness and competitiveness of the proposed module.

翻译：摘要：近年来，中文拼写检查（CSC）通过设计特定任务的预训练方法或引入辅助任务而大幅改进，这些方法大多以端到端方式解决该任务。本文提出将CSC工作流程分解为检测、推理和搜索三个子任务，使有关中文语言的丰富外部知识得以更直接、更高效地利用。具体而言，我们设计了一个即插即用的检测与推理模块，该模块与现有最先进非自回归CSC模型兼容，可进一步提升其性能。我们发现，为一个模型训练的检测与推理模块也能惠及其他模型。此外，我们还研究了任务分解所提供的基本可解释性。大量实验和详细分析证明了所提模块的有效性和竞争力。