한국어 무주어 구문의 영어 번역 양상: 인간번역, 구글번역, 챗GPT 간의 차이를 중심으로
Strategies for translating zero-subject Korean sentences into English: A focus on the differences between Human, NMT, and LLM Translations.
- 발행
- 2024 등록이 논문은 발행 시점을 확인하지 못해 원문 등록일을 적었습니다. KCI가 2005~2008년에 옛 논문을 몰아서 올린 탓에, 그 시기 등록분은 발행보다 평균 3~5년 늦습니다. 실제 발행연도는 더 이를 수 있습니다.
- 소속·발행
- 이화여자대학교
- 출처
- 국내 KCI
- DOI
- 10.24303/lakdoi.2024.32.3.1
- 원문
- 원문 보기 ↗
개념
키워드
기계번역, 신경망기계번역, 대규모언어모델, 무주어, 한영번역, machine translation, neural machine translation, large language model, zero subject, Korean-English translation
초록
This paper investigates the translation strategies employed in human and machine translations of Korean zero-subject sentences into English. The author translated 343 zero-subject segments from management forewords in business reports using Google Translate (NMT) and GPT-3.5 (LLM) and compared the results with quality human translation, seeking to investigate the patterns of three corpora’s translation strategies—subject restoration or structural modification. It was found that all three corpora-human translation (HT), NMT, and LLM translation-the dropped subject was most commonly replaced by personal pronouns rather than other nouns. Two statistically significant differences emerged among the corpora. First, HT exhibited a higher frequency of proper or general noun subjects, likely reflecting translators' efforts to avoid repetitive use of the first-person plural pronoun "we" in adjacent sentences. In contrast, NMT and LLM translations frequently adopted "we," leveraging it as a safe choice to enhance reader engagement in this genre. Second, NMT showed an overuse of short passive constructions without an agent, a choice underrepresented in LLM translations. While short passives ca