Improved Neural Machine Translation with Chinese Phonologic Features

Abstract

Chinese phonologic features play an important role not only in the sentence pronunciation but also in the construction of a native Chinese sentence. To improve the machine translation performance, in this paper we propose a novel phonology-aware neural machine translation (PA-NMT) model where Chinese phonologic features are leveraged for translation tasks with Chinese as the target. A separate recurrent neural network (RNN) is constructed in NMT framework to exploit Chinese phonologic features to help facilitate the generation of more native Chinese expressions. We conduct experiments on two translation tasks: English-to-Chinese and Japanese-to-Chinese tasks. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on these two tasks.