학부번역전공자의 기계번역 포스트에디팅, 무엇이 문제이고, 무엇을 가르쳐야 하는가?
What can we learn from trainee translators’ post-editing?
0
조회
0
다운로드
76
인용
0
참고문헌
키워드
초록
The purpose of this study is two-fold: (1) to show what problems exist in machine translation post-editing performed by translation students and (2) to suggest what should be considered in an undergraduate course on post-editing. To achieve this purpose, the researcher analysed data from a real post-editing project in which five junior students majoring in English-Korean translation post-edited Google Translate output. The source text is a medical text (consisting of about 780,000 words), written by a U.S. medical centre for the benefit of the general public. The product of the students’ post-editing was to be used for a smartphone application that would provide multilingual diagnosis services for ordinary people. The post-editing output was evaluated, using Mossop’s (2014) revision parametres: accuracy, completeness, logic, facts, smoothness, tailoring, sub-language, idiom, mechanics, layout, typography, and organisation. A close analysis of sample data revealed four problems: the students (1) performed monolingual post-editing in an inappropriate manner, (2) tried to resolve word-level issues without considering co-text, (3) paid scant attention to unnatural or ungrammatical language forms, and (4) had great difficulties in revising (translating) medical terminology. The implications of the findings for post-editor training are also briefly mentioned in the context of undergraduate translator training.
Abstract
The purpose of this study is two-fold: (1) to show what problems exist in machine translation post-editing performed by translation students and (2) to suggest what should be considered in an undergraduate course on post-editing. To achieve this purpose, the researcher analysed data from a real post-editing project in which five junior students majoring in English-Korean translation post-edited Google Translate output. The source text is a medical text (consisting of about 780,000 words), written by a U.S. medical centre for the benefit of the general public. The product of the students’ post-editing was to be used for a smartphone application that would provide multilingual diagnosis services for ordinary people. The post-editing output was evaluated, using Mossop’s (2014) revision parametres: accuracy, completeness, logic, facts, smoothness, tailoring, sub-language, idiom, mechanics, layout, typography, and organisation. A close analysis of sample data revealed four problems: the students (1) performed monolingual post-editing in an inappropriate manner, (2) tried to resolve word-level issues without considering co-text, (3) paid scant attention to unnatural or ungrammatical language forms, and (4) had great difficulties in revising (translating) medical terminology. The implications of the findings for post-editor training are also briefly mentioned in the context of undergraduate translator training.
참고문헌 (18)
- 최효은, "특허 기계번역 결과물의 평가 - KIPRIS의 무료 한영 기계번역을 중심으로", 통역과 번역, 19(1), pp.139~178 (2017), 한국통역번역학회
- Carl, M., "The process of post-editing: A pilot study", 7th Workshop on Statistical Machine Translation (2011)
- Doherty, S., "Assessing the usability of raw machine translated output: A user-centered study using eye-tracking", International Journal of Human-Computer Interaction, 30, pp.40~51 (2014)
- Fiederer, R., "Quality and machine translation: A realistic objective?", The Journal of Specialised Translation, 11, pp.52~74 (2009)
- Flanagan, M., "Testing post-editing guidelines: How translation trainees interpret them and how to tailor them for translator training purposes", The Interpreter and Translator Trainer, 8(2), pp.257~275 (2014)
- Garcia, I., "Is machine translation ready yet?", Target, 22(1), pp.7~21 (2010)
- Koponen, M., "Is machine translation post-editing worth the effort? A survey of research into post-editing and effort", The Journal of Specialised Translation, 25, pp.131~148 (2016)
- Lewis, D., "Machine translation in a modern languages curriculum", Computer Assisted Language Learning, 10(3), pp.255~271 (1997)
- Mossop, B., "Revising and Editing for Translators" (2014), Routledge
- Niño, A., "Evaluating the use of machine translation post-editing in the foreign language class", Computer Assisted Language Learning, 21(1), pp.29~49 (2008)
- O’Brien, S., "Teaching post-editing: A proposal for course content", 6th EAMT Workshop Teaching Machine Translation, pp.99~106 (2002)
- Plitt, M., "A productivity test of statistical machine translation post-editing in a typical localisation context", The Prague Bulletin of Mathematical Linguistics, 93, pp.7~16 (2010)
- Pym, A., "Translation skill-sets in a machine-translation age", Meta, 58(3), pp.487~503 (2013)
- "Using MT post-editing for translator training. Tralogy II, Session 6 - Teaching around MT / Didactique, enseignement, apprentissage" (2011)[URL]
- Temizöz, Ö., "Postediting machine translation output: Subject- matter experts versus professional translators", Perspectives, 24(4), pp.646~665 (2016)
- Vieira, L. N., "Cognitive effort and different task foci in post-editing of machine translation: A think-aloud study", Across Languages and Cultures, 18(1), pp.79~105 (2017)
- "진화하는 번역 앱 … 번역가 → 에디터 역할로 바뀐다" (2017)[URL]
- "‘파파고’ 총괄 “AI 번역, 7년 뒤 일반인 수준 넘는다" (2017)[URL]