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Chinese Spelling Correction (CSC) is a task to detect and correct spellingmistakes in texts. In fact, most of Chinese input is based on pinyin inputmethod, so the study of spelling errors in this process is more practical andvaluable. However, there is still no research dedicated to this essentialscenario. In this paper, we first present a Chinese Spelling Correction Datasetfor errors generated by pinyin IME (CSCD-IME), including 40,000 annotatedsentences from real posts of official media on Sina Weibo. Furthermore, wepropose a novel method to automatically construct large-scale and high-qualitypseudo data by simulating the input through pinyin IME. A series of analysesand experiments on CSCD-IME show that spelling errors produced by pinyin IMEhold a particular distribution at pinyin level and semantic level and arechallenging enough. Meanwhile, our proposed pseudo-data construction method canbetter fit this error distribution and improve the performance of CSC systems.Finally, we provide a useful guide to using pseudo data, including the datascale, the data source, and the training strategy.

CSCD-IME: Correcting Spelling Errors Generated by Pinyin IME