# Neural Machine Translation of Electrical Engineering with Fusion of Memory Information
## 1 Introduction  
With the rapid development of the information age, virtual connections across various fields around the world have been established through the internet [1]. The application of various scientific fields has gradually increased, and communication between different fields has become more frequent. The language barrier between different scientific fields has become a bottleneck for academic communication, which has greatly hindered the development of technology. Machine translation is a field in natural language processing, which is defined as the process of translating words, sentences, paragraphs, and entire texts from one language to another [2], and it plays a crucial role in the development of various scientific fields.  
In 2013, [PERSON] and [PERSON] proposed a new end-to-end encoder-decoder structure for machine translation, which gave rise to neural machine translation [3]. In the following years, recursive neural networks [4], convolutional neural networks [5], and transformers [6] were successively proposed, among which the transformers have the most comprehensive performance and have become the focus of many researchers in recent years.  
Research has shown that transformers have achieved great success on large corpora, but their performance is relatively poor on language pairs with limited training data (also known as low resource) [7]. The main reason for this phenomenon is that models trained on general corpora cannot correctly translate specialized semantics in some specific fields,and the same word often has vastly different meanings in different fields. Therefore, designing suitable neural machine translation models for specific fields is extremely necessary. This paper focuses on English-to-Chinese translation in the field of electrical engineering, which is a typical low-resource neural machine translation task. Many scientific fields require the application of knowledge related to electrical engineering, and designing a translation model that is adapted to the field of electrical engineering can facilitate academic communication between different countries and promote technological development. Considering that the traditional transformer may result in ambiguity when translating complex sentences with strong specialization, this paper proposes a memory information encoder to improve this situation.  
The main contributions of this paper are as follows:  
In order to optimize the performance of the transformer in translating complex sentences and compensate for its weakness in capturing long-term dependencies in language sequences, a new encoder structure is proposed by combining a GRU with an attention mechanism and integrating it into the model structure. This effectively improves the translation quality and enhances the translation performance of the model.  
Different fusion methods are explored and analyzed to find the most suitable fusion method for memory information and global information.  
Ablation experiments are designed to investigate the impact of key components on the model's performance. Comparative experiments are also designed to test the proposed model on a dataset in the field of electrical engineering and compare it with baseline models and other advanced models under the same experimental conditions, proving the superior performance of the proposed model.