Neural Machine Translation of Electrical Engineering Based on Vector Fusion

Abstract

The development of neural machine translation has achieved a good translation effect on large-scale general corpora, but there are still many problems in the translation of low resources and specific fields. This paper studies the problem of machine translation in the field of electrical engineering and fuses the multi-layer vectors at the encoder side of the model. On this basis, the decoder unit of the translation model is improved, and a multi-attention mechanism translation model based on vector fusion is proposed, which improves the ability of the model to extract features and achieves a better translation effect on Chinese-English translation tasks. The experimental results show that the BLEU (bilingual evaluation understudy) value of the improved translation system in the field of electrical engineering has increased by 0.15–1.58 percentage points.