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KCI 등재

A Data-driven Taxonomy of Self-repairs in Chinese-English Consecutive Interpreting

통번역학연구
약어 : -
2020 vol.24, no.4, pp.207 - 238
DOI : 10.22844/its.2020.24.4.207
발행기관 : 한국외국어대학교 통번역연구소
연구분야 : 통역번역학
Copyright © 한국외국어대학교 통번역연구소
당방(唐芳) 1

1중국 광동외어외무대학 통번역대학(广东外语外贸大学高级翻译学院)

인용한 논문 수 :   -  
17 회 열람

Understanding features of self-repairs is critical to the exploration of interpreters’ underlying self-monitoring mechanism. As most previous taxonomies just made slight amendments on Levelt’s tripartite taxonomy model — which is designed for monolingual speech production, they failed to match features of interpreting output. In order to resolve the defects of previous classifications, the present paper established an interpreting-tailored taxonomy of self-repairs, which categorized repairs into five major types with altogether nine sub-types. Rather than directly referring to Levelt’s model, each category in the new taxonomy is derived from the descriptive study of data collected from ten interpreting trainees, including their interpreting recordings, retrospection and notes. The qualitative analysis on distribution of each repair category also shows that trainees’ adoption of repairs is mainly triggered by competence deficiency rather than the attempt to facilitate communication.

self-repairs, taxonomy, data-driven approach, Chinese-English consecutive interpreting, interpreting trainees
자기발화수정, 분류학, 데이터 중심 접근, 중국어-영어 순차통역, 통역 학습자

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