英文摘要 |
This article re-looks into machine translation (MT) errors and proposes a function-oriented MT post-editing (MTPE) typology in a new technological context. Driven by the technological advances of the neural machine translation (NMT) system over the past several years, the author thinks that we should re-examine MT errors created by NMT systems, and understand whether the NMT system can resolve the issues the rule-based MT (RBMT) and statistical MT (SMT) systems have encountered. A mixed-methods approach is used to complete this study, and technical texts, journalistic texts and web-based company texts are chosen as analytical materials. The three-phased procedure consists of (1) cross-checking the differences between source texts (STs), MT outputs and corresponding human translations (HTs) to identify MT errors, (2) proposing a three-tier MTPE typology to supplement the current binary MTPE typology and (3) exploring empirical and theoretical implications of this research. The findings differ from previous MTPE studies in three aspects: (1) amending linguistic, pragmatic and affective MT errors with the strategies of ''accurateenough editing,'' ''clear-enough editing'' and ''attractive-enough editing,'' not the strategies of light editing and full editing; (2) replacing the existing editor-driven MTPE typology with a functiondriven MTPE typology; and (3) using a progressive, flexible MTPE typology to meet the textual functions of different types of MT texts. Overall, this article re-examines MT errors and MTPE strategies, and raises an alternative MTPE typology from the perspective of textual functions in the framework of the NMT scenario. It expects to add some novel insights to contemporary MT studies. |