Developing Post-editing Strategies for Machine-translated Chinese Texts: A Case Study on Gynaecological Cancer Information

Abstract

Concomitant with the increased development of the Internet, the number of users in China has increased, and more people now use it to access health information. The incidence of gynaecological cancer (GC) in China is on the rise. However, the online availability of information concerning GC in China is limited compared with that available in the United Kingdom, the United States of America, Australia, Canada, and New Zealand. The lack of this information limits people’s awareness of healthcare. As deep-learning techniques have developed, neural machine translation (NMT) has become a mainstream approach in machine translation (MT). Free-to-use tools offer a low-cost and highly efficient language-translation solution. However, despite continued advancement in NMT technology in processing texts from English to Chinese, errors persist in scientific and technical texts, especially those of a medical nature. Even when accounting for people’s socio-economic status, language barriers that limit communication can significantly affect health. By incorporating human post-editing, MT could reduce the time and cost required to translate public health materials from English to Chinese that maintain a similar quality to human translation. By doing so, the availability of multilingual public health materials would be significantly improved. Inaccurate, ambiguous, unnatural, or non-inclusive use of translated language may generate misunderstanding regarding information in translations. In medical texts, such as those pertaining to GCs, this could lead to inappropriate decisions being made, and even negative impacts on psychological or mental health. Therefore, the objectives of research presented herein are to develop post-editing strategies to deal with machine translations from English to Chinese of medical texts, and specifically texts pertaining to GC, based on language use guides for cancer information. In doing so, the accuracy, clarity, and naturalness of health information is improved, and that information available to Chinese-speaking people with cancer, and their families and friends, would be more positive and supportive, and public health awareness would be improved. To achieve these objectives, online health information was sourced from health departments and organisations in the United States of America, United Kingdom, Canada, Australia, and New Zealand (e.g., the National Cancer Institute, Cancer Research UK, Canadian Cancer Society, Cancer Council Australia, and The Cancer Society of New Zealand). By way of qualitative analysis, the limitations of English to Chinese machine-translation of GC information (accuracy at lexical and syntactic levels, logical coherence, lexical and syntactic ambiguities, idiomatic expression, and linguistic inclusiveness) are evaluated. New post-editing strategies of machine-translated texts are developed, and their ability to resolve various MT issues is demonstrated in a series of case studies.