Flick: Japanese Input Method Editor Using N-Gram and Recurrent Neural Network Language Model Based Predictive Text Input

Abstract

Smartphone is prevalent among many people. Smartphone is used not only by personal use but also by business. However, inputting Japanese text to smartphone requires longer time than PC. For this reason, predictive input, which suggesting next words, is important to type word efficiently. On the other hands, Recurrent Neural Networks (RNNs) are very powerful sequence models. Thus, we developed the input method editor (IME), which using n-gram and a recurrent neural networks language model based predictive text input. This IME is aimed at decreasing actions of inputting text. The evaluation experiments show our method outperforms conventional Japanese IME in terms of amount of time.