CAT 환경 내 번역 생산성 및 양상 연구 - 세그먼트 기반 인터페이스와 TM 일치율을 중심으로
Translation Productivity and Its Patterns in CAT Environments: Focusing on Segment-based Interfaces and TM Fuzzy Match Rates
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This study examines the quantitative impact of fuzzy match rates in translation memory (TM), a key function of computer-aided translation (CAT) tools, on translation productivity. In Experiment 1, the study compares CAT and non-CAT environments to assess how segment-based interfaces affect translation time and working patterns. Experiment 2 analyzes the influence of varying TM match rates on translation speed. The results show that while CAT environments had a limited effect on reducing overall translation time, they did affect patterns such as sentence merging and omission rates. High fuzzy match rates (above 85%) significantly reduced translation time, whereas lower match rates (60-74%) tended to increase cognitive load and editing effort. These findings offer empirical support for optimizing TM usage in CAT workflows, rate-setting practices in the translation industry, and translator training strategies. The study also identifies the need for follow-up research comparing TM-based translation with post-editing of machine translation output, and examining the effects of text genre and complexity on translation efficiency.
Abstract
This study examines the quantitative impact of fuzzy match rates in translation memory (TM), a key function of computer-aided translation (CAT) tools, on translation productivity. In Experiment 1, the study compares CAT and non-CAT environments to assess how segment-based interfaces affect translation time and working patterns. Experiment 2 analyzes the influence of varying TM match rates on translation speed. The results show that while CAT environments had a limited effect on reducing overall translation time, they did affect patterns such as sentence merging and omission rates. High fuzzy match rates (above 85%) significantly reduced translation time, whereas lower match rates (60-74%) tended to increase cognitive load and editing effort. These findings offer empirical support for optimizing TM usage in CAT workflows, rate-setting practices in the translation industry, and translator training strategies. The study also identifies the need for follow-up research comparing TM-based translation with post-editing of machine translation output, and examining the effects of text genre and complexity on translation efficiency.