機器翻譯是自然語言處理研究上最重要的課題之一。過去運用機器翻譯比較成功的例子，多是特定領域文件的翻譯。近來因網際網路與搜尋引擎的盛行，機器翻譯在跨語言檢索（Cross-Language Information Retrieval）中的角色開始受到重視。在跨語言檢索時，通常是對查詢字詞或片語進行翻譯（Query Translation），翻譯的結果對檢索的效果有很大的影響。我們希望透過統計式片語機器翻譯（Statistical Phrase Translation Model, SPTM）的做法來進行查詢關鍵詞的翻譯，以獲取良好的跨語言檢索效果。
過去的相關研究基本上可分為兩大類方法：統計為本做法和詞彙為本做法。在統計為本的做法中，以IBM Watson研究中心的Brown等（1988, 1990, 1993）提出的統計式機器翻譯做法，在理論上較為嚴謹，在架構與做法上較為明確可行。

我們提出一種新的模型來改進Brown的做法。新模型將Brown模型翻譯機率（Translation Probability）的三個機率函數──詞彙翻譯機率（Lexical Translation Probability）、孳生機率（Fertility Probability）、位置扭曲機率（Distortion Probability），轉化成兩個機率函數──詞彙翻譯機率及指派機率（Assignment Probability）。

我們以BDC漢英字典裡的65,078對名詞片語為語料，使用EM演算法做了一系列的實驗，並使用Och等人（2000）的評估方法來評估我們的實驗結果，得到召回率（Recall）為92.0%，準確率（Precision）為91.3%，錯誤率（Error Rate）為8.4%。我們亦研究中文斷詞及EM演算法的起始模型對實驗結果的影響，發現中文斷詞對訓練結果有小幅度幫助，而較佳的起始模型會得到較好的訓練結果。

Machine Translation is one of the most difficult problems in the field of natural language processing. In the past, MT has been applied to professional communication in the process of translating technical and corporate document in a specific domain. Recently, because of the rapid development of Internet and the need to access information across the language, people began to look into the role that MT can play in Cross Language Information Retrieval. The prevalent approach to CLIR is based on translation of query phrases. We propose a noval approach based on Statistical Phrase Translation Model (SPTM), aimed at achieving a tighter estimation of phrase translation probability.
Experiments were conducted using bilingual phrases in the BDC Electronic Chinese-English Dictionary. The training of alignment model is done by the EM-algorithm. For evaluation, we adapted the methodology used by Och et al. (2000) to assess the performance of the experiment. We obtained the recall rate of 92.0%, the precesion rate of 91.3% and the error rate of 8.4%.

The effect of Chinese segmentation and initial model of EM algorithm was also studied. We found that Chinese segmentation can improve the traning result slightly. A better initial model was found to improve the performance of the EM algorithm significantly.

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