Translation Service Implementation in Cloud: Automation Trends in English Translation Industry
Keywords:cloud computing, translation services, English translation, automation
INTRODUCTION: The development of information technology has led to the renewal of teaching methods, and cloud translation combining offline and online learning has become a trend in higher education.
OBJECTIVES: It is becoming increasingly apparent that the "surface issues" of blended learning are being addressed, especially the lack of consistency in online task development, which leads to inefficiencies in deep understanding.
METHODS: Through literature research, the factors affecting task planning in cloud translation are analyzed, and a cloud computing task planning model is established based on task learning theory.
RESULTS: The results show that task-based cloud translation can increase students' learning engagement and that targeted group task design is critical in improving students' interest and translation skills.
CONCLUSION: Using complex task modeling can improve the academic level of translation students and increase their involvement in translation projects.
Aitzhanova, K., Kerimkulova, S., Chsherbakov, A., Goodman, B., & Kambatyrova, A. (2022). Institutional Supports for Language Development through English‐Medium Instruction: A Factor Analysis. TESOL Quarterly, 56(2), 713–749. https://doi.org/10.1002/tesq.3090
Alsharari, N. M. (2022). Cloud computing and ERP assimilation in the public sector: Institutional perspectives. Transforming Government: People, Process and Policy, 16(1), 97–109. https://doi.org/10.1108/TG-04-2021-0069
Beames, L., Strodl, E., Dark, F., Wilson, J., & Kerswell, N. (2020). A Feasibility Study of the Translation of Cognitive Behaviour Therapy for Psychosis into an Australian Adult Mental Health Clinical Setting. Behavior Change, 37(1), 1–11. https://doi.org/10.1017/bec.2020.1
Chang, S.-Y. (2021). English Medium Instruction, English‐Enhanced Instruction, or English without Instruction: The Affordances and Constraints of Linguistically Responsive Practices in the Higher Education Classroom. TESOL Quarterly, 55(4), 1114–1135. https://doi.org/10.1002/tesq.3076
Díaz-Cintas, J., & Zhang, J. (2022). Going global against the tide:The translation of Chinese audiovisual productions. Babel, 68(1), 1–23. https://doi.org/10.1075/babel.00255.dia
Glasco, Dl., Ho, Nhb., Mamaril, Am., & Bell, Jg. (2021). 3D Printed Ion-Selective Membranes and Their Translation into Point-of-Care Sensors. Analytical Chemistry, 93(48), 15826–15831. https://doi.org/10.1021/acs.analchem.1c03762
Hayes, J. J. C., Kerins, E., Awiphan, S., Mcdonald, I., & Kittara, P. (2020). Optimizing exoplanet atmosphere retrieval using unsupervised machine-learning classification. Monthly Notices of the Royal Astronomical Society, 494(3), 4492–4508. https://doi.org/10.1093/mnras/staa978
Heesbeen, C., & Prieto, A. (2020). Archetypical CBMs in Construction and a Translation to Industrialized Manufacture. Sustainability, 12(4), 1572. https://doi.org/10.3390/su12041572
Huimin, L. (2020). Aesthetic Differences of Literary Works in the Process of Translation from Chinese to English. Linguistics, 2(3), 244–257. https://doi.org/10.35534/lin.0203022
Kabir, H. M. D., Khosravi, A., Mondal, S. K., Rahman, M., & Buyya, R. (2021). Uncertainty-aware Decisions in Cloud Computing: Foundations and Future Directions. ACM Computing Surveys, 54(4), 1–30. https://doi.org/10.1145/3447583
Konttinen, K. (2021). A self-efficacy scale for measuring student progress in translation company simulations. Across Languages and Cultures, 22(1), 64–81. https://doi.org/10.1556/084.2021.00004
Li, B., & He, Y. (2021). A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting. Discrete Dynamics in Nature and Society, 2021(1), 1–17.
Li, C., Yao, Y., Xu, M., Yang, J., Wang, S., Gao, X., Shao, S., Chen, X., Zheng, X., & Liu, Y. (2023). A unifying review of edge intelligent computing technique applications in the field of energy networks. Journal of Industrial and Management Optimization, 19(11), 7966–7992. https://doi.org/10.3934/jimo.2023027
Liu, K. (2021). On the Construction of Teachers' Professional Quality-oriented English Practice Teaching System—Exemplified with the English Major of Sichuan University of Arts and Science. Theory and Practice in Language Studies, 11(4), 390–395. https://doi.org/10.17507/tpls.1104.08
Powers, J. G., Werner, K. K., Gill, D. O., Lin, Y. L., & Schumacher, R. S. (2021). Cloud Computing Efforts for the Weather Research and Forecasting Model. Bulletin of the American Meteorological Society, 102(6), 1–38. https://doi.org/10.1175/BAMS-D-20-0219.1
Ramsay, G. (2020). A cloud-computing platform for developing and evaluating vocal biomarkers based on home audio recordings: Resources for large-scale data processing and analysis. The Journal of the Acoustical Society of America, 148(4), 2791–2791. https://doi.org/10.1121/1.5147769
Randles, S., Dewick, P., Rietbergen, M., Taylor, C., Vargas, V. R., Wadham, H., Nicholson, D. T., Keeler, L. W., & Hannan, E. (2022). Applying enquiry and problem based learning to mission-oriented innovation policy: From policy to pedagogy to teaching and learning practice. Journal of International Education in Business, 15(1), 52–73. https://doi.org/10.1108/JIEB-04-2021-0046
Tang, W., Yang, Q., Hu, X., & Yan, W. (2022). Deep learning-based linear defects detection system for large-scale photovoltaic plants based on an edge-cloud computing infrastructure. Solar Energy, 231(16), 527–535. https://doi.org/10.1016/j.solener.2021.11.016
Williams, R. B. (2020). Helena Willoughby’s English translation of Lamouroux’s Histoire des polypiers coralligènes flexibles and her new word “polypidom.” Archives of Natural History, 47(1), 183–186. https://doi.org/10.3366/anh.2020.0630
Xu, X., & Lockwood, J. (2021). What's going on in the chat flow? A move analysis of e-commerce customer service webchat exchange. English for Specific Purposes, 61(10), 84–96. https://doi.org/10.1016/j.esp.2020.09.002
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