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  • 學位論文

自動學習運用於機器翻譯之樣式文法

Learning Synchronous Pattern Grammar for Machine Translation

指導教授 : 張俊盛
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摘要


在本論文,我們提出了一個同步樣式文法自動學習方法,用於輔助語言學習者 翻譯語句至另一個語言。我們首先由平行語料庫的英文語句抽取英文的樣式文法, 接著在中文語句找出對應的中文樣式文法。在英文樣式文法部分,我們將英文句 子轉成一系列的短語,將短語中的字詞轉換成樣式元素,最後找出有效的英文的 動、名、形容詞的樣式文法。我們提出了一個以機器翻譯為模型的中文斷詞器。在 斷詞之後,透過機器翻譯的字詞配對,最終抽取出同步樣式文法。 藉由同步樣式文法,我們建立了一個互動式線上寫作環境 — WriteAhead for translators。其自動顯示與輸入區的游標或滑鼠指標位置字詞相關的同步文法樣式 與範例,作為翻譯的範例與提示。

並列摘要


In this paper, we introduce a method for learning Synchronous Pattern Grammar (SPG) for assisting learners in translating sentences from one language into another. In our approach, we learn English pattern grammar from a given corpus, and then project the pattern grammar to Chinese through a parallel corpus with alignment annotation. The method involves converting English sentences into sequences of phrase chunks, converting phrase chunks to pattern elements, and extracting salient patterns for content words (verbs, noun, and adjective). The method also involves developing a machine-translation based Chinese word segmenter, developing a base phrase clunker, and converting bilingual phrases to synchronous grammar patterns. With synchronous grammar patterns, we present an interactive writing environ- ment, WriteAhead for translators, that automatically extracts and displays relevant synchronous grammar patterns with examples to prompt the user as they translate or mouse around a translation draft during editing.

參考文獻


Brown, Peter F et al. (1990). “A statistical approach to machine translation”. In: Computational linguistics 16.2, pp. 79–85.
Chen, Mei-Hua et al. (2011). “A cross-lingual pattern retrieval framework”. In: Pro- ceedings of the CICLing.
Church, Kenneth Ward and Patrick Hanks (1990). “Word association norms, mutual information, and lexicography”. In: Computational linguistics 16.1, pp. 22–29.
Ellis, Peter Beresford, Susan Hunston, and Elizabeth Manning (1996). Grammar Patterns 1: Verbs (COBUILD). Collins CoBUILD. isbn: 0003750620.
Goldberg, Adele E (2009). “The nature of generalization in language”. In: Cognitive Linguistics 20.1, pp. 93–127.

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