機器翻譯的發展超過60年，卻仍無法取代人工翻譯。翻譯記憶加上自動翻譯平台逐漸的成熟技術，發展出以機器翻譯搭配人工後編譯的翻譯模式。此種翻譯模式能較過去提高翻譯產量同時提昇譯文品質，也因此讓翻譯公司與專業譯者提高了使用機器翻譯的意願。惟機器翻譯所產出的譯文錯誤，導致後編譯作業耗時費工，譯文品質不穩定。
為深入探討導致後編譯費時耗工譯文品質不穩定的原因，進一步提昇後編譯效率的方法，本研究提出四個問題：(1)導致機器翻譯譯文難以閱讀的主因為何？ (2)機器輔助人工翻譯後編譯效率的瓶頸為何？(3)針對釐清機器翻譯譯文錯置的問題，後編譯譯者需要什麼樣的訓練？(4)針對機器翻譯譯文後編譯訓練，語言剖析器(Language Parser)能提供什麼樣的協助？
研究方法以Google Translate (GT)為機器翻譯模型，輸入五種不同類型的文本產出譯文，再將原文輸入經過內建語言學語法的語言剖析器進行剖析，兩者結果對照分析後，歸納出GT譯文與原文語意的分歧與錯誤後，再以前述分岐錯誤總結出包含40項要點的後翻譯因應策略清單。
本研究最終發現：(1)導致機器翻譯譯文難以閱讀的主因為譯文單詞順序錯置(reordering)；(2)譯文單詞順序一錯亂，讓後編譯譯需花費大把時間調整順序，影響工作效率，而成為最大瓶頸；(3)後編譯譯者最需要的訓練就是要能看出造成譯文錯亂的原因是來自事件(events)的數量與事件間(relation-events)的關係，同時加強語言學理論與機器翻譯概念基礎。因此，本研究歸納出19項訓練項目，能協助後編譯譯者一眼看出譯文為何錯置的原因；(4)本研究以語言剖析器剖析原文歸納出40項要點(checklist)、七項指南(guidelie)及19項針對後編譯技能的訓練課程(training program)，後編譯譯者參照這些項目由淺入深，經過相當時間的訓練，應能有效加快後編譯的速度同時維持譯文品質。

Machine translation (MT) has been gone along for over sixty years, and gradually develops a translation combination of Machine Translation (MT) and Post-Editing (PE). Although this combination successfully increases the productivity and quality of translation, some problems, like word sense disambiguation and reordering, derived from MT still make PE efficiency impossible.
In order to solve above MT problems, this study raises up four study questions: (1) what are the major problems that affect the quality of current MT? (2) what are the bottlenecks that compromise the efficiency of post-editing in MAHT? (3) what kinds of training are needed to enable post-editors to address the problems derived from MT’s poor reordering? (4) how can a language parser help with the training to untangle cluttered output texts from MT in post-editing training?
This study uses Google Translate (GT) as the testing model to firstly produce the output out of different sample texts. Then the GT texts were compared with the original texts (OT) parsed with a language parser to identify the errors between GT and OT. Finally, those problems were summarized and, as a result, produce a checklist of 40 checkpoints and 7 guidelines. Moreover, around 19 training goals are laid out for the reference of post-editor training programs.
According to the results, four research questions of this study are answered: (1) the major problems that affect the quality of current MT are WSD and reordering, mainly reordering; (2) the bottlenecks that compromise the efficiency of post-editing in MAHT is reordering; (3) instilling and enhancing post-editors’ perspective of linguistic are needed to address the problems derived from MT’s poor reordering; (4) the guideline and checkpoints can help the training to untangle cluttered output texts from MT in post-editing training. All in all, with the aid of the checklist, the translators will gradually improve their PE skills, as well as the productivity and quality of translation.

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