Usability of Computer-Aided Translation Software Based on Deep Learning Algorithms

Abstract

In recent years, due to the development of computer technology and information technology, web technology has changed the mode of translation at an alarming rate. The rapid development of information technology and globalization has increased the demand for translation, especially technical translation, and the use of computer-assisted translation software can greatly improve the quality and efficiency of translation work. The purpose of this article is that under the premise of continuous advancement in computer technology, computer-assisted translation can effectively improve the translation efficiency of translators and the quality of translated text. This article references the practicality of computer translation software as the benchmark and uses computer-aided translation software based on deep learning as the core. At the same time, it introduces the current popular microservice concept to build an electronic computer-assisted translation software based on microservice architecture. Based on the performance of the system, the high availability and scalability of the system are enhanced, so that the entire system can provide stable and efficient computer-assisted translation services for users. At the same time, the usability test method is used to compare and evaluate two common computer-aided translation software, Trados and Wordfast. By observing, recording, and analyzing user behavior and related data, the five attributes of usability can be learned and memorable. The experiments show that the effect of this study on computer-aided software with the help of deep learning knowledge can produce good results, and the robustness and scalability of the software have been enhanced, increasing the competitiveness of the software itself in translation software.