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Chinese pinyin input methods are very important for Chinese languageprocessing. Actually, users may make typos inevitably when they input pinyin.Moreover, pinyin typo correction has become an increasingly important task withthe popularity of smartphones and the mobile Internet. How to exploit theknowledge of users typing behaviors and support the typo correction for acronympinyin remains a challenging problem. To tackle these challenges, we proposeKNPTC, a novel approach based on neural machine translation (NMT). In contrastto previous work, KNPTC is able to integrate explicit knowledge into NMT forpinyin typo correction, and is able to learn to correct a variety of typoswithout the guidance of manually selected constraints or languagespecificfeatures. In this approach, we first obtain the transition probabilitiesbetween adjacent letters based on large-scale real-life datasets. Then, weconstruct the "ground-truth" alignments of training sentence pairs by utilizingthese probabilities. Furthermore, these alignments are integrated into NMT tocapture sensible pinyin typo correction patterns. KNPTC is applied to correcttypos in real-life datasets, which achieves 32.77% increment on average inaccuracy rate of typo correction compared against the state-of-the-art system.

KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction