On the Role of Expertise in Applying MTPE Mode in Medical Translation

With the continuous development of neural network technology and AI translation in specialized fields, machine translation + post-editing (MTPE) has become the predominant mode of translation practice. Medical texts are often complex, containing specialized terminology, nuanced language, and context-specific information that require a deep understanding of both the source and target languages. Expertise in the field ensures that translators can effectively assess and refine the machine-generated translations, and correct inaccuracies thus ensuring a quality output. This paper first reviews the literature on the application of the MTPE mode to specialized texts. Then, it summarizes four types of mistranslation based on translation practice, namely inaccuracies, unprofessionalism, redundancy, nonfluency, inconsistency, inconherency, and incompleteness. Furthermore, case analyses at the lexical, sentence, and discourse levels are illustrated to discuss the collaboration between machine translation and human editing. The study emphasizes the need to enhance the adaptability of neural machine translation models in specialized fields, while human translators should also continually enhance their specialized language skills and solidify their domain knowledge base to effectively carry out post-editing, proofreading, and modifications.