A Smart Sliding Chinese Pinyin Input Method Editor on Touchscreen
This addresses input efficiency for Chinese users on touchscreen devices, representing an incremental improvement with automated optimization based on user behavior.
The paper tackles the problem of inefficient Chinese character input on touchscreens by developing a smart sliding pinyin IME that predicts characters during finger sliding and adapts the keyboard layout using deep learning, resulting in improved user input efficiency as verified empirically.
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.