Article: Practical application of social science text translation based on the computer-assisted translation platform YiCAT Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.71 pp.104 - 127 Abstract: With increasing demand for international dissemination of social science works, computer-assisted translation tools face severe challenges when dealing with culturally-loaded words and abstract concepts. Existing general platforms lack terminology consistency and contextual adaptability. Therefore, based on the Yi Computer-Assisted Translation platform, this study constructed an enhanced workflow integrating a dynamic terminology database and a context verification mechanism. Experimental results on the public United Nations parallel corpus subset show that compared to the original yi computer-assisted translation baseline, this workflow increased translation accuracy by 12% and improved key term consistency by 18%; compared to pure manual translation, it increased translation efficiency by approximately 35% while maintaining 90% semantic fidelity. The results indicate that the proposed enhanced solution improves social science translation quality and human-computer collaboration efficiency, providing a reusable technical path for related field practices. Inderscience Publishers - linking academia, business and industry through research

Title: Practical application of social science text translation based on the computer-assisted translation platform YiCAT

Authors: Ru Shen

Addresses: Department of General Education, Luoyang Polytechnic, Luoyang, 471000, China

Abstract: With increasing demand for international dissemination of social science works, computer-assisted translation tools face severe challenges when dealing with culturally-loaded words and abstract concepts. Existing general platforms lack terminology consistency and contextual adaptability. Therefore, based on the Yi Computer-Assisted Translation platform, this study constructed an enhanced workflow integrating a dynamic terminology database and a context verification mechanism. Experimental results on the public United Nations parallel corpus subset show that compared to the original yi computer-assisted translation baseline, this workflow increased translation accuracy by 12% and improved key term consistency by 18%; compared to pure manual translation, it increased translation efficiency by approximately 35% while maintaining 90% semantic fidelity. The results indicate that the proposed enhanced solution improves social science translation quality and human-computer collaboration efficiency, providing a reusable technical path for related field practices.

Keywords: computer-assisted translation; CAT; social science texts; human-computer collaboration; quality assessment.

DOI: 10.1504/IJICT.2026.154476

International Journal of Information and Communication Technology, 2026 Vol.27 No.71, pp.104 - 127

Received: 20 Feb 2026
Accepted: 03 Apr 2026
Published online: 29 Jun 2026 *

Title: Practical application of social science text translation based on the computer-assisted translation platform YiCAT

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