Statistical Feature Aided Intelligent Deep Learning Machine Translation in Internet of Things

Internet of Things (IoT) networks have been widely deployed to achieve communication among machines and humans. Machine translation can enable human-machine interactions for IoT equipment. In this paper, we propose to combine the neural machine translation (NMT) and statistical machine translation (SMT) to improve translation precision. In our design, we propose a hybrid deep learning (DL) network that uses the statistical feature extracted from the words as the data set. Namely, we use the SMT model to score the generated words in each decoding step of the NMT model, instead of directly processing their outputs. These scores will be converted to the generation probability corresponding to words by classifiers and used for generating the output of the hybrid MT system. For the NMT, the DL network consists of the input layer, embedding layer, recurrent layer, hidden layer, and output layer. At the offline training stage, the NMT network is jointly trained with SMT models. Then at the online deployment stage, we load the fine-trained models and parameters to generate the outputs. Experimental results on French-to-English translation tasks show that the proposed scheme can take advantage of both NMT and SMT methods, thus higher translation precision could be achieved.