A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for
  Chinese Spelling Check
Haojing Huang Jingheng Ye Qingyu Zhou Yinghui Li Yangning Li Feng Zhou Hai-Tao Zheng
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.