Neural Network Language Model for Chinese Pinyin Input Method Engine

Neural network language models (NNLMs) have been shown to outperform traditional ngram language model. However, too high computational cost of NNLMs becomes the main obstacle of directly integrating it into pinyin IME that normally requires a real-time response. In this paper, an efficient solution is proposed by converting NNLMs into back-off n-gram language models, and we integrate the converted NNLM into pinyin IME. Our experimental results show that the proposed method gives better decoding predictive performance for pinyin IME with satisfied efficiency.