Inducing Sentence Structure from Parallel Corpora for Reordering

Abstract

When translating among languages that differ substantially in word order, machine transla-tion (MT) systems benefit from syntactic pre-ordering—an approach that uses features from a syntactic parse to permute source words into a target-language-like order. This paper presents a method for inducing parse trees au-tomatically from a parallel corpus, instead of using a supervised parser trained on a tree-bank. These induced parses are used to pre-order source sentences. We demonstrate that our induced parser is effective: it not only improves a state-of-the-art phrase-based sys-tem with integrated reordering, but also ap-proaches the performance of a recent pre-ordering method based on a supervised parser. These results show that the syntactic structure which is relevant to MT pre-ordering can be learned automatically from parallel text, thus establishing a new application for unsuper-vised grammar induction. 1