A Comparative Study of Human Translation and Machine Translation with Post-Editing

人工翻譯與機器翻譯加後編輯之比較

10.29912/CTR.201109.0005

李家璿(Ja-Son Lee)；廖柏森(Po-Sen Liao)

機器翻譯 ； 後編輯 ； machine translation ； post-editing ； Google Translate

編譯論叢

4卷2期（2011 / 09 / 01）

105 - 149

英文

日新月異的科技演進已大幅增進機器翻譯（machine translation）的品質，成為翻譯時的輔助工具。然而我們目前對於人工翻譯與自動機器翻譯系統加後編輯的差異仍缺乏足夠認識，因此本研究旨在比較這兩種翻譯模式的異同與過程。研究方法以不同英文程度的兩組大學生共140人為研究對象，請他們翻譯英文版的手機保養指南。兩組中各有一部份學生只拿到英文原文，另一部份學生則多了機器翻譯的中文譯文以進行後編輯。考量機器翻譯使用的便利性及免付費，故中文譯文取自於Google Translate。研究中記錄研究對象翻譯過程所花費的時間，並利用錯誤分析評估其譯文表現。統計結果顯示，使用機器翻譯的譯文能夠顯著減少某些學生的翻譯錯誤，亦能縮減學生英文程度落差對於譯文表現的影響。此外，質性分析更進一步闡明學生如何使用機器翻譯譯文的過程，以及不同組別之間的譯文差異。這些機器翻譯後端的分析可說明人工應用機器輔助翻譯的實際情況，也指出學生翻譯上的問題，希望能作為日後機器翻譯研究與教學的參考。

With rapid advances in technology, many improvements have been made to make machine translation (MT) an effective tool for translators in the process of translation. However, an important issue that has not been extensively investigated is the use of automatic MT systems with postediting in comparison to human translation. The purpose of the present study, therefore, is to explore the similarities, differences, and processes of these two modes of translation. In terms of research methods, two groups of college students with different English proficiency levels were asked to translate a cell phone user guide written in English. In each group, some students received only the English source text while others received the source text in addition to a machine-translated Chinese text for postediting. Google Translate was the MT system used in this study because of its easy and free accessibility. A total of 140 subjects were timed on their translation tasks, and their performance was determined by the number of errors in the translated text. The statistical results indicated that the MT text was very helpful in reducing errors in some student translations; the use of MT also shortened the gap between students of divergent language proficiency levels. Further qualitative analysis elucidated how the MT text was utilized and the discrepancies in lexical choice and other aspects between the two groups of students. This back-end analysis of the machine translation process may offer insights into the practical use of human-assisted machine translation, as well as problems encountered by students when translating. It is hoped that these results may serve as a basis to facilitate future machine translation studies and teaching.

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