In this thesis, we introduce a method for translating out-of-vocabulary (OOV) words. The model focuses on combining the sublexical translations of an OOV to generate suitable translations. In our approach, we formulate wildcard search in the phrase translation table to find sublexical translations, which are then combined to form translation candidates, aimed at maximizing the probability of translating an OOV word. These translation candidates are ranked and filtered based on bilingual and monolingual information. We have incorporated the proposed OOV model into the state-of-the-art phrase-based machine translation system performing Chinese-to-English translation tasks. Experimental results show that the OOV module indeed helps to improve the quality of translation, especially for sentences containing more OOV words. Our methodology generates translations for the OOV words that can be incorporated into machine translation systems to ease the negative impact of OOV on translation quality.

本論文提出一個翻譯未知詞的方法，可用於解決機器翻譯中的未知詞問題。產生翻譯的方法，主要是利用未知詞中各組成字的翻譯，使用不同的方式組合以產生該未知詞的翻譯。方法的主要步驟，是從機器翻譯系統原有的詞彙翻譯表中，以未知詞的組成字搭配萬用符號進行查詢，取得各組成字的翻譯，進而由組成字翻譯中組合出該未知詞的翻譯。我們利用雙語與單語資料來篩選和排序本方法產生的未知詞翻譯。本研究的測試方式，是將產生的未知詞翻譯與現有的機器翻譯系統整合，進行中文翻譯至英文的實驗。在評估方面，我們使用BLEU準則來進行評分。實驗結果顯示，當中文句含有較多的未知詞時，本系統提供的未知詞翻譯能改善整體的翻譯品質。本論文的主要貢獻在於，我們的方法利用機器翻譯系統現有的詞彙翻譯表，逐字翻譯未知詞的組成字，並從中組合出未知詞的翻譯，進而幫助解決機器翻譯中的未知詞問題。

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