利用依存關係之辭彙翻譯

Word Translation Disambiguation via Dependency

本論文提出了一個利用依存關係解決詞彙翻譯的新方法。我們的方法包含了訓練階段及測試階段。在訓練階段，取得與實詞具依存關係的搭配字，並在這些依存關係的條件下，學習分辨翻譯歧義的決策表（decision list）。在測試階段，對於句子中每個實詞檢查跟其有依存關係的搭配字。在測試階段，比對決策表，給予這些字一個正確翻譯。我們實際撰寫了程式，並利用香港新聞及香港立法會議記錄作為訓練資料。在實驗中我們用了五種不同的方法去處理測試資料並透過一個自動的擬似BLEU的評估方法去比較實驗結果。由實驗結果顯示，依存關係的確可以顯著的幫助詞彙翻譯，而實驗也證實某些依存關係是比其他的依存關係更具影響力的。

We introduce a new method for automatically disambiguation of word translations by using dependency relationships. In our approach, we learn the relationships between translations and dependency relationships from a parallel corpus. The method consists of a training stage and a runtime stage. During the training stage, the system automatically learns a translation decision list based on source sentences and its dependency relationships. At runtime, for each content word in the given sentence, we give a most appropriate Chinese translation relevant to the context of the given sentence according to the decision list. We also describe the implementation of the proposed method using bilingual Hong Kong news and Hong Kong Hansard corpus. In the experiment, we use five different ways to translate content words in the test data and evaluate the results based an automatic BLEU-like evaluation methodology. Experimental results indicate that dependency relations can obviously help us to disambiguate word translations and some kinds of dependency are more effective than others.