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

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4275 - 4286 December 7-11, 2022 ©2022 Association for Computational Linguistics Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity Jiahao Li1, Quan Wang2∗, Zhendong Mao1, Junbo Guo3, Yanyan Yang4, Yongdong Zhang1 1University of Science and Technology of China, Hefei, China 2MOE Key Laboratory of Trustworthy Distributed Computing and Service, Beijing University of Posts and Telecommunications, Beijing, China 3People’s Daily Online Co., Beijing, China 4People’s Public Security University of China, Beijing, China jiahao66@mail.ustc.edu.cn, wangquan@bupt.edu.cn, zdmao@ustc.edu.cn guojunbo@people.cn, zhyd73@ustc.edu.cn Abstract Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling er- rors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effec- tively modeling the pronunciation of Chinese characters is a key factor for CSC. In this pa- per, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systemati- cally 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 improve- ments during inference. Empirical evaluation shows that SCOPE achieves new state-of-the- art on three CSC benchmarks, demonstrating the effectiveness and superiority of the auxil- iary 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 https: //github.com/jiahaozhenbang/SCOPE. 1 Introduction Chinese Spelling Check (CSC), which aims to de- tect and correct spelling errors in Chinese texts, is a fundamental task in Chinese natural language pro- cessing. Spelling errors mainly originate from hu- man writing errors and machine recognition errors, e.g., errors caused by automatic speech recognition (ASR) and optical character recognition (OCR) sys- tems (Huang et al., 2021). With the latest develop- ment of deep neural networks, neural CSC methods, ∗Corresponding author: Quan Wang. Instance Similarity Coarse Fine W: 我觉得你们会好好的完(wan2/w,an,2)。 1 1 I think you will finish well. R: 我觉得你们会好好的玩(wan2/w,an,2)。 I think you will play well. W:我以前想要高(gao1/g,ao,1)诉你。 0 2/3 I tried to high you before. R: 我以前想要告(gao4/g,ao,4)诉你。 I tried to tell you before. W:他收(shou1/sh,ou,1)到山上的时候。 0 1/3 When he received the mountain. R: 他走(zou3/z,ou,3)到山上的时候。 When he walked up the mountain. W:行为都被蓝(lan2/l,an,2)控设备录影。 0 0 Actions are recorded by blue control devices. R: 行为都被监(jian1/j,ian,1)控设备录影。 Actions are recorded by surveillance devices. Table 1: Instances from SIGHAN15 (Tseng et al., 2015). For each instance, coarse-/fine-grained pinyin of the mis- spelled (red) and correct (blue) characters are provided, along with their phonological similarity degree (the frac- tion of identical components) in terms of these two types of pinyin. in particular those based on encoder-decoder archi- tectures, have become the mainstream of research in recent years (Xu et al., 2021; Liu et al., 2021). …

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