臺灣博碩士論文加值系統

隨著網路使用人口成長，網站在地化的需求也持續增加，而為了以更快速度提供品質穩定的翻譯成果，將自動翻譯融入在地化專案原有的工作流程已是必然趨勢。另一方面，近年來由於網路發展迅速讓語料庫取得更加容易，也促進了機器翻譯技術的發展，實現自訂神經機器翻譯模型的應用。然而目前自訂神經機器翻譯的相關研究多半仍集中在改善技術層面，因此本研究將會將重心放在將自訂神經機器翻譯系統應用於在地化專案，並且採用自動化方法與專業譯者參與實驗的方式評估其效能與效果。在建立自訂神經機器翻譯系統方面，本研究從風格類似的三個快時尚品牌網站中，分別擷取出繁體中文與英文的產品介紹文字檔，彙整成雙語平行語料庫，再將母語料庫分為訓練與驗證兩個語料庫，用於訓練與檢驗自訂神經機器翻譯系統。而除了採用自動化方法衡量機器翻譯的效能，本研究也招募專業譯者參與模擬實際在地化專案的實驗，比較自訂機器翻譯與一般機器翻譯的效果。研究結果顯示，本研究建立的自訂神經機器翻譯經過自動化方法評估可達到良好效能，而在專業譯者的使用者經驗方面，自訂神經機器翻譯確實有一定的效果，能協助提升譯者的工作效率。儘管本研究指出將神經機器翻譯應用於在地化專案的可行性，由於研究中選擇的語料僅侷限在快時尚購物網站，研究結果可能無法在其他在地化領域上成立，而適用於其他翻譯領域的自訂神經機器翻譯系統則需要將來進一步的研究。

As the number of internet users grows around the world, the demand for website localization also increases. To meet this ever-growing need, a trend has inevitably formed where automatic translation is being integrated into the workflow of localization. In recent years, the development of the internet has provided easy access to various corpora, which advances the technology of machine translation and, in turn, realizes the application of customized neural machine translation (NMT). Nevertheless, previous studies on customized NMT usually center on improving the technology itself. Thus, this research focuses on applying a customized NMT to website localization and validating its performance and effects with automatic evaluation and an experiment involving human translators. To build customized NMT, the bilingual text from shopping websites including H&M, ZARA and Burberry is compiled into a parallel corpus, which is divided into two separate corpora which train and evaluate the customized NMT. In addition to automatic evaluation, this study also carries out an experiment aiming to compare the effectiveness of the customized NMT and that of general MT which was modeled on real localization projects which involved professional translators,. The automatic evaluation result indicates that the NMT built in this research performs well. Moreover, based on the feedback from translators participating in this study, the customized NMT does exhibit positive effects on increasing translation efficiency. Due to the fact that the corpus used in this research is limited to a specific domain, the results might not be applicable to other localization fields, and further research is needed to investigate the effectiveness of customized NMT in other domains.

Acknowledgment ii
Abstract iii
中文摘要 iv
List of Figures vii
List of Tables viii
Chapter One: Introduction 1
Chapter Two: Literature Review 6
2.1 Localization 7
2.2 Computer-aided Translation 10
2.3 Corpus-based Studies 11
2.4 Machine Translation 13
2.4.1 Rule-based machine translation 14
2.4.2 Corpus-based machine translation 15
2.4.3 Neural machine translation 17
2.5 Evaluation on machine translation 19
Chapter Three: Methodology 22
3.1. Corpus compilation 25
3.1.1 Data from H&M websites 25
3.1.2 Data from ZARA websites 27
3.1.3 Data from Burberry websites 28
3.1.4 Text pre-processing 28
3.2. Building customized NMT: OpenNMT 29
3.3. Automatic Evaluation: BLEU 32
3.4. English Corpora analysis 33
3.4.1 Lexical characteristics 34
3.4.2 Composition per line 35
3.5. Computer-aided translation 35
3.5.1 Building translation memory 36
3.5.2 Pre-translation with CAT tools 37
3.6 Experiment: Comparison between NMT, Google Translate and Trados 38
3.6.1 Selecting two groups of source text 39
3.6.2 Creating translation projects in Trados 41
3.6.3 Designing a questionnaire for feedback 43
3.6.4 Handing out projects and instructions 45
3.6.5 Retrieving results 47
Chapter Four: Results and Discussion 48
4.1 Performance of the customized NMT 48
4.1.1 Automatic Evaluation: BLEU 48
4.1.2 Training corpus analysis 51
4.1.3 CAT statistics analysis 52
4.2 Perceived effects of the customized NMT 57
4.3 Empirical effects of the customized NMT 62
4.3.1 Translators’ working time 63
4.3.2 Translators’ editing actions 65
Chapter Five: Conclusion and Limitation 74
5.1 Summary and reflection 74
5.2 Limitation 76
5.3 Future research 76
References 78
Appendices 83

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