Author:
Liang Jingjing,Ma Pianpian
Abstract
In order to facilitate communication and communication, this paper studies the optimization of the current computer-aided translation system, and proposed a design method of English communication language computer-aided translation system based on grey clustering evaluation. By optimizing the hardware configuration and algorithm function keys of the system, the English translation mechanism of multilanguage interaction, the design idea of editing and modifying after English translation and knowledge database management are realized, and the system function framework was constructed, including the system transceiver unit, automatic translation unit, manual correction unit, task management unit and memory management unit, the performance of task management unit and memory management unit is analyzed. On this basis, the specific work flow of the design system mainly includes the English translation service flow integrating multilanguage interaction and the project-based multilanguage interaction English translation service flow design, which realizes the English translation online assistance under the multilanguage interaction environment. The experimental results show that the design system has the advantages of high online translation speed, pronunciation success rate and multilanguage translation success rate.
Subject
Computational Mathematics,Computer Science Applications,General Engineering
Reference23 articles.
1. P.J. Last, H.A. Engelbrecht and H. Kamper, Unsupervised feature learning for speech using correspondence and Siamese networks, IEEE Signal Processing Letters 27(10) (2020), 421–425.
2. S.S. Gao, K.H. Liu, X.F. Fu, D.A.H.M. Haider, F.S. Kong, L. Liu, R.E.E.D. Cory, M.C. Sun and Y.Q. Yu, Structural seismology: exploring the correspondence between surface geological features and heterogeneities in the earth’s crust and mantle, Abstracts of the 9th World Chinese Conference of Geological Science (2019), 2.
3. H. Raheli, R.M. Rezaei, M.R. Jadidi and H.G. Mobtaker, A two-stage DEA model to evaluate sustainability and energy efficiency of tomato production, Information Processing in Agriculture 4(4) (2017), 342–350.
4. Efficient phrase table pruning for Hindi to English machine translation through syntactic and marker-based filtering and hybrid similarity measurement;Chatterjee;Natural Language Engineering,2019
5. Post-editing neural machine translation versus phrase-based machine translation for English-Chinese;Jia;Machine Translation,2019