Parse trees are indispensable to the existing tree-based translation models. However, there exist two major challenges in utilizing parse trees: 1) For most language pairs, it is hard to get parse trees due to the lack of syntactic resources for training. 2) Numerous parse trees are not compatible with word alignment which is generally learned by GIZA++. Therefore, a number of useful translation rules are often excluded. To overcome these two problems, in this paper we make a great effort to bypass the parse trees and induce effective unsupervised trees for tree-based translation models. Our unsupervised trees depend only on the word alignment without utilizing any syntactic resource or linguistic parser. Hence, they are very beneficial for the translation between resource-poor languages. Our experimental results have shown that the string-to-tree translation system using our unsupervised trees significantly outperforms the string-to-tree system using parse trees.
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