  This paper presents a smart sliding Chinese pinyin Input Method Editor (IME)
for touchscreen devices which allows user finger sliding from one key to
another on the touchscreen instead of tapping keys one by one, while the target
Chinese character sequence will be predicted during the sliding process to help
user input Chinese characters efficiently. Moreover, the layout of the virtual
keyboard of our IME adapts to user sliding for more efficient inputting. The
layout adaption process is utilized with Recurrent Neural Networks (RNN) and
deep reinforcement learning. The pinyin-to-character converter is implemented
with a sequence-to-sequence (Seq2Seq) model to predict the target Chinese
sequence. A sliding simulator is built to automatically produce sliding samples
for model training and virtual keyboard test. The key advantage of our proposed
IME is that nearly all its built-in tactics can be optimized automatically with
deep learning algorithms only following user behavior. Empirical studies verify
the effectiveness of the proposed model and show a better user input
efficiency.
