earticle

영어

At the present time, when the development of neural machine translation presents a significant turning point, this paper attempts to compare NMT with existing statistically-based machine translation, and discuss implications for future translator education. Multiple types of MT errors and mistranslations are identified by analysis. This study intends to propose a codified table of those error types as a tool for objective assessment and ongoing monitoring of the quality of MT. Based on a translation editing scheme for learners, error types at lexical, phrasal, syntactic and textual levels were identified, which were then re-sorted for MT. The classification was applied to the analysis of translation of informative texts and news articles, for different error types, MT platforms (Naver, Google) and language directions, to draw graphs to demonstrate the results. Considering that the work of translation in the future will likely evolve into a combination of MT with human post-editing, continuous MT quality assessment would be necessary. The outcome of this study would help post-editors figure out where to focus working with different MT platforms, which is potentially useful for translator training.