Neural Machine Translation of Electrical Engineering Based on Integrated Convolutional Neural Networks

Abstract

Research has shown that neural machine translation performs poorly on low-resource and specific domain parallel corpora. In this paper, we focus on the problem of neural machine translation in the field of electrical engineering. To address the mistranslation caused by the Transformer model’s limited ability to extract feature information from certain sentences, we propose two new models that integrate a convolutional neural network as a feature extraction layer into the Transformer model. The feature information extracted by the CNN is fused separately in the source-side and target-side models, which enhances the Transformer model’s ability to extract feature information, optimizes model performance, and improves translation quality. On the dataset of the field of electrical engineering, the proposed source-side and target-side models improved BLEU scores by 1.63 and 1.12 percentage points, respectively, compared to the baseline model. In addition, the two models proposed in this paper can learn rich semantic knowledge without relying on auxiliary knowledge such as part-of-speech tagging and named entity recognition, which saves a certain amount of human resources and time costs.