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Chinese spelling check (CSC) is a fundamental NLP task that detects andcorrects spelling errors in Chinese texts. As most of these spelling errors arecaused by phonetic similarity, effectively modeling the pronunciation ofChinese characters is a key factor for CSC. In this paper, we considerintroducing an auxiliary task of Chinese pronunciation prediction (CPP) toimprove CSC, and, for the first time, systematically discuss the adaptivity andgranularity of this auxiliary task. We propose SCOPE which builds on top of ashared encoder two parallel decoders, one for the primary CSC task and theother for a fine-grained auxiliary CPP task, with a novel adaptive weightingscheme to balance the two tasks. In addition, we design a delicate iterativecorrection strategy for further improvements during inference. Empiricalevaluation shows that SCOPE achieves new state-of-the-art on three CSCbenchmarks, demonstrating the effectiveness and superiority of the auxiliaryCPP task. Comprehensive ablation studies further verify the positive effects ofadaptivity and granularity of the task. Code and data used in this paper arepublicly available at https://github.com/jiahaozhenbang/SCOPE.

Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity