원문정보
초록
영어
As the artificial intelligence (AI) plays a crucial role in machine translation (MT) which has loomed large as a new translation paradigm, concerns have also arisen if MT can produce a quality product as human translation (HT) can. In fact, several MT experimental studies report cases in which the MT product called post-editing (PE) as equally as HT or often superior ([1],[2],[6]). As motivated from those studies on translation quality between HT and PE, this study set up an experimental situation in which Korean literature was translated into English, comparatively, by 3 translators and 3 post-editors. Afterwards, a group of 3 other Koreans checked for accuracy of HT and PE; a group of 3 English native speakers scored for fluency of HT and PE. The findings are (1) HT took the translation time, at least, twice longer than PE. (2) Both HT and PE produced similar error types, and Mistranslation and Omission were the major errors for accuracy and Grammar for fluency. (3) HT turned to be inferior to PE for both accuracy and fluency.
목차
1. Introduction
2. Related works
3. Present research
3.1 Research questions
3.2 Content profiles and adjustments
3.3 Translator (HT) and post-editor (PE) profiles
3.4 MT platform: VisualTran of EverTran Co., Ltd.
3.5 Translation processes
3.6 Evaluation criteria
3.7 Evaluator profiles for accuracy and fluency
4. Results
4.1 Accuracy comparison
4.2 Fluency comparison
5. Summary
Acknowledgement
References