  Chinese Spelling Check (CSC) aims to detect and correct potentially
misspelled characters in Chinese sentences. Naturally, it involves the
detection and correction subtasks, which interact with each other dynamically.
Such interactions are bi-directional, i.e., the detection result would help
reduce the risk of over-correction and under-correction while the knowledge
learnt from correction would help prevent false detection. Current CSC
approaches are of two types: correction-only or single-directional
detection-to-correction interactive frameworks. Nonetheless, they overlook the
bi-directional interactions between detection and correction. This paper aims
to fill the gap by proposing a Bi-directional Detector-Corrector framework for
CSC (Bi-DCSpell). Notably, Bi-DCSpell contains separate detection and
correction encoders, followed by a novel interactive learning module
facilitating bi-directional feature interactions between detection and
correction to improve each other's representation learning. Extensive
experimental results demonstrate a robust correction performance of Bi-DCSpell
on widely used benchmarking datasets while possessing a satisfactory detection
ability.
