Earticle

초록

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.

목차

Abstract
 I. 서론
 II. 선행연구 예문의 신경망 기계번역 검토
 III. 한일/일한 기계번역에 대한 교열코드 적용
  1. 선행연구의 오류 항목 검토
  2. 오류 항목 검토
  3. 일한/한일 번역의 오류 코드표
 IV. 결론
 참고문헌

키워드

저자

• 이주리애 [ Lee, JuRiAe | 이화여자대학교 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  통역과 번역 [Interpretation and Translation]

• 간기

  연3회

• pISSN

  1229-6074

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 717 DDC 400