Dependency Tree Abstraction for Long-Distance Reordering in Statistical Machine Translation

Word reordering is a crucial technique in statistical machine translation in which syntactic information plays an important role. Synchronous context-free grammar has typically been used for this purpose with various modifications for adding flexibilities to its synchronized tree generation. We permit further flexibilities in the synchronous context-free grammar in order to translate between languages with drastically different word order. Our method pre-processes a parallel corpus by abstracting source-side dependency trees, and performs long-distance reordering on top of an off-the-shelf phrase-based system. Experimental results show that our method significantly outperforms previous phrase-based and syntax-based models for translation between English and Japanese.