ArticleJMIR public health and surveillance
Machine Translation of Public Health Materials From English to Chinese: A Feasibility Study.
Article in JMIR public health and surveillance. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Development of a Large-Scale Dataset of Chest Computed Tomography Reports in Japanese and a High-Performance Finding Classification Model: Dataset Development and Validation Study.JMIR medical informatics · 2025Article
- Article
- The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review.JMIR public health and surveillance · 2023Article
- Validation testing of a language translation device for suitability in assisting Australian radiation therapists to communicate with Mandarin-speaking patients.Technical innovations & patient support in radiation oncology · 2023Article
- Evaluation of a Language Translation App in an Undergraduate Medical Communication Course: Proof-of-Concept and Usability Study.JMIR mHealth and uHealth · 2021Article
- Predicting the Linguistic Accessibility of Chinese Health Translations: Machine Learning Algorithm Development.JMIR medical informatics · 2021Article
- Evaluating Local Multilingual Health Care Information Environments on the Internet: A Pilot Study.International journal of environmental research and public health · 2021Article
- Evaluating the Usefulness of Translation Technologies for Emergency Response Communication: A Scenario-Based Study.JMIR public health and surveillance · 2019Article
- Public Health, Population Health, and Epidemiology Informatics: Recent Research and Trends in the United States.Yearbook of medical informatics · 2017Review
- Machine or Human? Evaluating the Quality of a Language Translation Mobile App for Diabetes Education Material.JMIR diabetes · 2017Article
- Evaluating the Accuracy of Google Translate for Diabetes Education Material.JMIR diabetes · 2016Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundChinese is the second most common language spoken by limited English proficiency individuals in the United States, yet there are few public health materials available in Chinese. Previous studies have indicated that use of machine translation plus postediting by bilingual translators generated quality translations in a lower time and at a lower cost than human translations.
objectiveThe purpose of this study was to investigate the feasibility of using machine translation (MT) tools (eg, Google Translate) followed by human postediting (PE) to produce quality Chinese translations of public health materials.
methodsFrom state and national public health websites, we collected 60 health promotion documents that had been translated from English to Chinese through human translation. The English version of the documents were then translated to Chinese using Google Translate. The MTs were analyzed for translation errors. A subset of the MT documents was postedited by native Chinese speakers with health backgrounds. Postediting time was measured. Postedited versions were then blindly compared against human translations by bilingual native Chinese quality raters.
resultsThe most common machine translation errors were errors of word sense (40%) and word order (22%). Posteditors corrected the MTs at a rate of approximately 41 characters per minute. Raters, blinded to the source of translation, consistently selected the human translation over the MT+PE. Initial investigation to determine the reasons for the lower quality of MT+PE indicate that poor MT quality, lack of posteditor expertise, and insufficient posteditor instructions can be barriers to producing quality Chinese translations.
conclusionsOur results revealed problems with using MT tools plus human postediting for translating public health materials from English to Chinese. Additional work is needed to improve MT and to carefully design postediting processes before the MT+PE approach can be used routinely in public health practice for a variety of language pairs.
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