Innovative methods for integrating translation memory and CAT tools: Enhancing intelligent support in human translation processes

Jiazhen Zhang; Lin Zhou; Wei Bai

doi:10.54254/2755-2721/90/2024MELB0054

Research Article

Open access

Published on 27 August 2024

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Zhang,J.;Zhou,L.;Bai,W. (2024). Innovative methods for integrating translation memory and CAT tools: Enhancing intelligent support in human translation processes. Applied and Computational Engineering,90,1-7.

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Innovative methods for integrating translation memory and CAT tools: Enhancing intelligent support in human translation processes

Jiazhen Zhang ¹, Lin Zhou ², Wei Bai *^,3,

¹ Liaoning Petrochemical University, Liaoning, China
² Taylor’s University, Selangor, Malaysia
³ hanghai Maritime University, Shanghai, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/90/2024MELB0054

Abstract

This paper explores the innovative integration of Translation Memory (TM) and Computer-Assisted Translation (CAT) tools to enhance translation efficiency, consistency, and quality in multinational organizations. By adopting a user-centric interface design, modular system architecture, and cloud-based deployment, the integrated system addresses diverse translation needs while ensuring data security and privacy. Key features include real-time translation suggestions powered by machine learning algorithms, seamless access to TM databases, and real-time collaboration tools. The paper discusses implementation strategies, challenges, and solutions, highlighting the importance of user training and continuous improvement. A case study demonstrates significant improvements in translation speed, accuracy, and user satisfaction, underscoring the potential of advanced translation technologies to transform translation workflows. The findings provide valuable insights into best practices for successful implementation and optimization of TM and CAT tools in complex, large-scale environments.

Keywords

Translation Memory, Computer-Assisted Translation, Machine Learning, Real-Time Collaboration, User-Centric Design

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References

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Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://2024.confcds.org/

ISBN：978-1-83558-609-9(Print) / 978-1-83558-610-5(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Ammar Alazab

Series: Applied and Computational Engineering

Volume number: Vol.90

ISSN：2755-2721(Print) / 2755-273X(Online)

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