  Chinese pinyin input method engine (IME) converts pinyin into character so
that Chinese characters can be conveniently inputted into computer through
common keyboard. IMEs work relying on its core component, pinyin-to-character
conversion (P2C). Usually Chinese IMEs simply predict a list of character
sequences for user choice only according to user pinyin input at each turn.
However, Chinese inputting is a multi-turn online procedure, which can be
supposed to be exploited for further user experience promoting. This paper thus
for the first time introduces a sequence-to-sequence model with gated-attention
mechanism for the core task in IMEs. The proposed neural P2C model is learned
by encoding previous input utterance as extra context to enable our IME capable
of predicting character sequence with incomplete pinyin input. Our model is
evaluated in different benchmark datasets showing great user experience
improvement compared to traditional models, which demonstrates the first
engineering practice of building Chinese aided IME.
