메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Lilian Chacha (University of Nairobi) John Habwe (University of Nairobi)
저널정보
한국외국어대학교 아프리카연구소 Asian Journal of African Studies Asian Journal of African Studies Vol.53
발행연도
2022.8
수록면
3 - 18 (16page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
In this study, we investigated challenges faced by computer assisted translation software with special focus on Google Translate, in translating animal metaphors from English to Kiswahili. The data for this study were sourced from William Shakespeare"s play, “The Tragedy of Othello the Moor of Venice” which has been translated to Kiswahili as “Othello, Tanzia ya Mtu Mweusi.” The data was informed by the Relevance-theoretic translation approach as postulated by Gutt (1991). To evaluate the quality of Google Translate computer assisted translation system, we made a comparison of the computer translated output with the human translated text to ascertain to what extent the meaning of the animal metaphors in the source language is preserved in the target text. We further examined challenges encountered by Google Translate in the process of translating animal metaphors and suggested what could be done to improve Google Translate method to ensure accuracy in translating metaphors. The results indicate that, there is inferior translation quality of the target text with ambiguous words and sentences. Also, it was observed that it is challenging to translate animal metaphors using Google Translate because it has not been programmed to process aspects of source culture or adapt to the aspects of target culture thus cannot correctly translate metaphors. Besides that, other challenges posed range from lexical, syntactic, and semantic to pragmatic mismatch.

목차

Abstract
1. Background
2. Representation and Translation of Metaphors
3. Methodology
4. Theoretical Orientation
5. Findings and Discussions
6. Conclusion
References

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2022-309-001689961