통번역대학원 번역 전공 학습자의 기계번역 활용 양상 및 인식에 대한 질적연구
A Qualitative Study on How Graduate Students Use and Perceive Machine Translation: Focusing on Their Error Correction Types and Perception toward Translation Machine Performance
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With the rapid advancement of translation technologies such as Google Translate, Papago, and ChatGPT, the user base and application scope of machine translation are expanding swiftly. Given their high performance and efficiency, discussions often revolve around the integration of post-editing training within graduate-level translation education. However, counterarguments propose that fostering students’ translation competence inherently nurtures their post-editing capabilities. Against this backdrop, this study seeks to ascertain whether students can proficiently engage in post-editing without prior specialized training. To achieve this objective, this research involves 11 students participating in a translation classroom setting. They are introduced to various translation machine tools and guided to incorporate these tools into their translation processes. The translation machines they employ and their methodologies are observed. Their translations and reflective commentaries are collected to analyze the types of error corrections made. Additionally, a survey is conducted to corroborate the findings derived from the error correction analysis. The findings reveal that the error corrections made during machine-assisted translation align with recommended post-editing guidelines. Furthermore, these corrections address deficiencies identified in previous post-editing studies. The remaining errors primarily stem from insufficient translation skills rather than inadequate post-editing proficiency. This underscores the necessity of prioritizing the development of translation competence over exclusive post-editing training. The study also underscores the growing enthusiasm among students to leverage machine translation technology as a means to augment their translation capabilities, which should be reflected in the design of machine translation classes.
Abstract
With the rapid advancement of translation technologies such as Google Translate, Papago, and ChatGPT, the user base and application scope of machine translation are expanding swiftly. Given their high performance and efficiency, discussions often revolve around the integration of post-editing training within graduate-level translation education. However, counterarguments propose that fostering students’ translation competence inherently nurtures their post-editing capabilities. Against this backdrop, this study seeks to ascertain whether students can proficiently engage in post-editing without prior specialized training. To achieve this objective, this research involves 11 students participating in a translation classroom setting. They are introduced to various translation machine tools and guided to incorporate these tools into their translation processes. The translation machines they employ and their methodologies are observed. Their translations and reflective commentaries are collected to analyze the types of error corrections made. Additionally, a survey is conducted to corroborate the findings derived from the error correction analysis. The findings reveal that the error corrections made during machine-assisted translation align with recommended post-editing guidelines. Furthermore, these corrections address deficiencies identified in previous post-editing studies. The remaining errors primarily stem from insufficient translation skills rather than inadequate post-editing proficiency. This underscores the necessity of prioritizing the development of translation competence over exclusive post-editing training. The study also underscores the growing enthusiasm among students to leverage machine translation technology as a means to augment their translation capabilities, which should be reflected in the design of machine translation classes.
참고문헌 (25)
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