Research Article
Statistical Feature Aided Intelligent Deep Learning Machine Translation in Internet of Things
@INPROCEEDINGS{10.1007/978-3-030-67514-1_20, author={Yidian Zhang and Lin Zhang and Ping Lan and Wenyong Li and Dan Yang and Zhiqiang Wu}, title={Statistical Feature Aided Intelligent Deep Learning Machine Translation in Internet of Things}, proceedings={IoT as a Service. 6th EAI International Conference, IoTaaS 2020, Xi’an, China, November 19--20, 2020, Proceedings}, proceedings_a={IOTAAS}, year={2021}, month={1}, keywords={Neural machine translation Statistical machine translation Neural network Statistical feature extraction}, doi={10.1007/978-3-030-67514-1_20} }- Yidian Zhang
Lin Zhang
Ping Lan
Wenyong Li
Dan Yang
Zhiqiang Wu
Year: 2021
Statistical Feature Aided Intelligent Deep Learning Machine Translation in Internet of Things
IOTAAS
Springer
DOI: 10.1007/978-3-030-67514-1_20
Abstract
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.
