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.

物联网 (IoT) 网络已被广泛部署以实现机器和人类之间的通信。机器翻译可以实现物联网设备的人机交互。在本文中，我们建议将神经机器翻译 (NMT) 和统计机器翻译 (SMT) 结合起来以提高翻译精度。在我们的设计中，我们提出了一个混合深度学习 (DL) 网络，该网络使用从单词中提取的统计特征作为数据集。也就是说，我们使用 SMT 模型在 NMT 模型的每个解码步骤中对生成的单词进行评分，而不是直接处理它们的输出。这些分数将被分类器转换为单词对应的生成概率，用于生成混合机器翻译系统的输出。对于 NMT，DL 网络由输入层、嵌入层、循环层、隐藏层和输出层。在离线训练阶段，NMT网络与SMT模型联合训练。然后在在线部署阶段，我们加载经过精细训练的模型和参数以生成输出。在法语到英语翻译任务上的实验结果表明，所提出的方案可以同时利用 NMT 和 SMT 方法，从而可以实现更高的翻译精度。