Main Article Content
The study reports early-stage research on the efficacy of using the source text pre-editing (STPE) method to improve translation accuracy and cost-efficiency in conjunction with Google Translate as compared to the traditional target text post-editing (TTPE) method. Based on fluency, accuracy, cultural appropriateness and error severity, preliminary results show that STPE significantly increased the meaning adequacy and accuracy in translation as compared to TTPE. STPE also saved significant time, and, therefore, was more cost-efficient, as compared to TTPE. The results suggested a fundamentally new and more efficient method to the better employment of machine translation that differed from existing approaches. Governments and health providers may use the STPE plus Google Translate method more widely to reduce translation inaccuracy as well as to increase cost-efficiency, and provide more accessible information to culturally and linguistically diverse clients.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abdullah, H. (2014). Lost in translation: Obamacare en Español. CNN Politics. [Online URL: https://edition.cnn.com/2014/01/15/politics/obamacare-spanish-language-site/index.html] accessed on March 1, 2021.
Allen, J. (2003). Post-editing. In Computers and Translation: A Translator’s Guide (Somers, H. ed.), pp. 297-317, Amsterdam: John Benjamins Publishing.
Arenas, A. G. (2020). Pre-editing and post-editing. In The Bloomsbury Companion to Language Industry Studies (Angelone, E., Ehrensberger-Dow, M., and Massey, G., eds.), pp. 333-360. London: Bloomsbury.
Beh, T. H. K., and Canty, D. J. (2015). English and Mandarin translation using Google Translate software for pre-anaesthetic consultation. Anaesthesia and Intensive Care, 43(6), 792-793.
Bhatt, N. (2018). Google showcases Australian AI successes: conservation, language and health. Ausdroid. [Online URL: https://ausdroid.net/2018/05/31/google-showcases-australian-ai-successes-conservation-language-health/] accessed on March 1, 2021.
Chatterjee, R., Weller, M., Negri, M., and Turchi, M. (2015), Exploring the planet of the apes: a comparative study of state-of-the-art methods for MT automatic post-editing. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 156-161, Beijing, China.
Chen, X., Acosta, S., and Barry, A. E. (2016). Evaluating the accuracy of Google Translate for diabetes education material. JMIR Diabetes, 1(1), e3.
Dalzell, S. (2020). Federal government used Google Translate for COVID-19 messaging aimed at multicultural communities. ABC News. [Online URL: https://www.abc.net.au/news/2020-11-19/government-used-google-translate-for-nonsensical-covid-19-tweet/12897200] accessed on March 1, 2021.
Guo, J. W. (2016). Is Google Translate adequate for facilitating instrument translation from English to Mandarin? Computers, Informatics, Nursing, 34(9), 377-383.
Khoong, E. C., Steinbrook, E., Brown, C., and Fernandez, A. (2019). Assessing the use of Google Translate for Spanish and Chinese translations of emergency department discharge instructions. JAMA Internal Medicine, 179(4), 580-582.
Lear, A., Oke, L., Forsythe, C., and Richards, A. (2016). “Why can't I just use Google Translate?” a study on the effectiveness of online translation tools in translation of Coas. Value in Health, 19(7), A387.
Lommel, A. R., and DePalma, D. A. (2016). Post-editing goes mainstream: how LSPs use MT to meet client demands. Cambridge MA: Common Sense Advisory.
Moberly, T. (2018). Doctors choose Google Translate to communicate with patients because of easy access. BMJ Clinical Research, 362, k3974.
Munday, J. (2016). Introducing Translation Studies: Theories and Applications, 4th, pp. 141-168. London: Taylor and Francis Group.
O’Brien, S. (2003). Controlling controlled English: an analytical of several controlled language rule sets. In Proceedings of EAMT-CLAW 2003, pp. 105-114, Dublin, Ireland.
Office of the Prime Minister of Australia. (2021). Over $1.1 billion to extend Australia’s covid-19 health response. [Online URL: https://www.pm.gov.au/media/over-11-billion-extend-australias-covid-19-health-response] accessed on 15 March 2021.
Patil, S., and Davies, P. (2014). Use of Google Translate in medical communication: evaluation of accuracy. BMJ, 349, g7392.
Petri, A. (2014). Spanish version of healthcare.gov apparently used computer translation? The Washington Post. [Online URL: https://www.washingtonpost.com/blogs/compost/wp/2014/01/13/spanish-version-of-healthcare-gov-apparently-used-computer-translation/] accessed on March 1, 2021.
Ponce, N. A., Hays, R. D., and Cunningham, W. E. (2006). Linguistic disparities in health care access and health status among older adults. Journal of General Internal Medicine, 21(7), 786-791.
Raynor, E. M. (2016). Factors affecting care in non-English-speaking patients and families. Clinical Pediatrics, 55(2), 145-149.
Sentell, T. L., Tsoh, J. Y., Davis, T., Davis, J., and Braun, K. L. (2015). Low health literacy and cancer screening among Chinese Americans in California: a cross-sectional analysis. BMJ Open, 5(1), e006104.
Turner, A. M., Dew, K. N., Desai, L., Martin, N., and Kirchhoff, K. (2015). Machine translation of public health materials from English to Chinese: a feasibility study. JMIR Public Health and Surveillance, 1(2), e17.
Van de Velde, S., Macken, L., Vanneste, K., Goossens, M., Vanschoenbeek, J., Aertgeerts, B., Vanopstal, K., Vander Stichele, R., and Buysschaert, J. (2015). Technology for large-scale translation of clinical practice guidelines: a pilot study of the performance of a hybrid human and computer-assisted approach. JMIR Medical Informatics, 3(4), e33.
Wade, R. G. (2011). Try Google Translate to overcome language barriers. BMJ, 343, d7217.