Abstract
Abstract This paper mainly carries out a more in-depth study on the design and planning of computer-assisted translation models based on translation memory and demonstrates the new computer-assisted translation model formed in this way. The TMX specification model, combined with the actual needs, summarizes the corresponding terminology data model, introduces and summarizes the word splitting, similarity, and alignment techniques of the computer-assisted translation model, and verifies the effectiveness of the model through practical application. The study shows that the average code length of computer-aided translation is about 0.1~0.3 lower than that of human translation in specialized fields, and its performance and efficiency are better than that of human translation. The efficiency of computer-aided translation software in extracting terminology positively correlates with an increase in frequency. Computer-assisted translation plays a significant role in the professional field and provides empirical support for subsequent related research.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
