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Pinyin-to-character (P2C) conversion is the core component of pinyin-basedChinese input method engine (IME). However, the conversion is seriouslycompromised by the ambiguities of Chinese characters corresponding to pinyin aswell as the predefined fixed vocabularies. To alleviate such inconveniences, wepropose a neural P2C conversion model augmented by an online updated vocabularywith a sampling mechanism to support open vocabulary learning during IMEworking. Our experiments show that the proposed method outperforms commercialIMEs and state-of-the-art traditional models on standard corpus and trueinputting history dataset in terms of multiple metrics and thus the onlineupdated vocabulary indeed helps our IME effectively follows user inputtingbehavior.

Open Vocabulary Learning for Neural Chinese Pinyin IME