Machine Translation of Public Health Materials From English to Chinese: A Feasibility Study

Anne M Turner; Kristin N Dew; Loma Desai; Nathalie Martin; Katrin Kirchhoff

doi:10.2196/publichealth.4779

Machine Translation of Public Health Materials From English to Chinese: A Feasibility Study

JMIR Public Health Surveill. 2015 Nov 17;1(2):e17. doi: 10.2196/publichealth.4779. eCollection 2015 Jul-Dec.

Authors

Anne M Turner^#¹, Kristin N Dew^#², Loma Desai³, Nathalie Martin^#⁴, Katrin Kirchhoff^#⁵

Affiliations

¹ Northwest Center for Public Health PracticeDepartment of Health ServicesUniversity of WashingtonSeattle, WAUnited States.
² Northwest Center for Public Health PracticeHuman Centered Design & EngineeringUniversity of WashingtonSeattle, WAUnited States.
³ Northwest Center for Public Health PracticeInformation SchoolUniversity of WashingtonSeattle, WAUnited States.
⁴ Northwest Center for Public Health PracticeDepartment of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattle, WAUnited States.
⁵ Speech, Signal and Language Interpretation (SSLI) LabDepartment of Electrical EngineeringUniversity of WashingtonSeattle, WAUnited States.

^# Contributed equally.

Abstract

Background: Chinese 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.

Objective: The 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.

Methods: From 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.

Results: The 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.

Conclusions: Our 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.

Keywords: Chinese language; consumer health; health literacy; health promotion; limited English proficiency; machine translation; natural language processing; public health; public health departments; public health informatics.