포스트에디팅 결과물의 정확성 오류 고찰 —AI 학습용 금융/증시 분야 한-영 번역 말뭉치를 대상으로—

Abstract

In sharp contrast to great attention to the quality of Machine Translation (MT) raw output, the quality of post-edited output has drawn relatively little attention in Korean translation studies, although some errors in MT output can remain even after post-editing. Against this backdrop, this study sets out to investigate accuracy errors in post-edited output, based on Korean-English parallel translation corpus for AI training released in June 2021 by the National Information Society Agency. For this purpose, 200 parallel sentences with accuracy errors were collected and classified by error type. According to the analysis results, mistranslation errors account for about two-thirds, with the rest in omissions, indicating that quite a number of omissions are still left in post-edited output. While lexical errors ranging from words to clauses are found most frequently in mistranslations, syntax errors represent a surprisingly large portion, with many errors in modifiers and subjects. This study draws attention to quality in MT post-editing, suggesting the need for further investigation into factors affecting the quality of post-edited output.