Chinese spelling check (CSC) is a fundamental NLP task that detects and
corrects spelling errors in Chinese texts. As most of these spelling errors are
caused by phonetic similarity, effectively modeling the pronunciation of
Chinese characters is a key factor for CSC. In this paper, we consider
introducing an auxiliary task of Chinese pronunciation prediction (CPP) to
improve CSC, and, for the first time, systematically discuss the adaptivity and
granularity of this auxiliary task. We propose SCOPE which builds on top of a
shared encoder two parallel decoders, one for the primary CSC task and the
other for a fine-grained auxiliary CPP task, with a novel adaptive weighting
scheme to balance the two tasks. In addition, we design a delicate iterative
correction strategy for further improvements during inference. Empirical
evaluation shows that SCOPE achieves new state-of-the-art on three CSC
benchmarks, demonstrating the effectiveness and superiority of the auxiliary
CPP task. Comprehensive ablation studies further verify the positive effects of
adaptivity and granularity of the task. Code and data used in this paper are
publicly available at this https URL.

本文提出了一种基于 SCOPE 的中文拼写纠错模型，该模型引入了中文发音预测辅助任务用于改进中文拼写检查，并在三个基准测试中实现了新的最高性能，证明了辅助任务的有效性和优越性。