When translating among languages that differ substantially in word order , machine translation ( MT ) systems benefit from syntactic preordering-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 automatically from a parallel corpus , instead of using a supervised parser trained on a treebank . These induced parses are used to preorder source sentences . We demonstrate that our induced parser is effective : it not only improves a state-of-the-art phrase-based system with integrated reordering , but also approaches the performance of a recent preordering 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 unsupervised grammar induction . Recent work in statistical machine translation ( MT ) has demonstrated the effectiveness of syntactic preordering : an approach that permutes source sentences into a target-like order as a pre-processing step , using features of a source-side syntactic parse ( Collins et al . , 2005 ; Xu et al . , 2009 ) . Syntactic pre-ordering is particularly effective at applying structural transformations , such as the ordering change from a subject-verb-object ( SVO ) language like English to a subject-object-verb ( SOV ) language like Japanese . However , state-of-the-art pre-ordering methods require a supervised syntactic parser to provide structural information about each sentence . We propose a method that learns both a parsing model and a reordering model directly from a word-aligned parallel corpus . Our approach , which we call Structure Induction for Reordering ( STIR ) , requires no syntactic annotations to train , but approaches the performance of a recent syntactic pre-ordering method in a large-scale English-Japanese MT system . STIR predicts a pre-ordering via two pipelined models : ( 1 ) parsing and ( 2 ) tree reordering . The first model induces a binary parse , which defines the space of possible reorderings . In particular , only trees that properly separate verbs from their object noun phrases will license an SVO to SOV transformation . The second model locally permutes this tree . Our approach resembles work with binary synchronous grammars ( Wu , 1997 ) , but is distinct in its emphasis on monolingual parsing as a first phase , and in selecting reorderings without the aid of a target-side language model . The parsing model is trained to maximize the conditional likelihood of trees that license the reorderings implied by observed word alignments in a parallel corpus . This objective differs from those of previous grammar induction models , which typically focus on succinctly explaining the observed source language corpus via latent hierarchical structure ( Pereira and Schabes , 1992 ; Klein and Manning , 2002 ) . Our convex objective allows us to train a feature-rich log-linear parsing model , even without supervised treebank data . Focusing on pre-ordering for MT leads to a new perspective on the canonical NLP task of grammar induction-one which marries the wide-spread scientific interest in unsupervised parsing models with a clear application and extrinsic evaluation methodology . To support this perspective , we highlight several avenues of future research throughout the paper . We evaluate STIR in a large-scale English-Japanese machine translation system . We measure how closely our predicted reorderings match those implied by hand-annotated word alignments . STIR approaches the performance of the state-of-the-art pre-ordering method described in Genzel ( 2010 ) , which learns reordering rules for supervised treebank parses . STIR gives a translation improvement of 3.84 BLEU over a standard phrase-based system with an integrated reordering model . We have demonstrated that induced parses suffice for pre-ordering . We hope that future work in grammar induction will also consider pre-ordering as an extrinsic evaluation .

Challenge: Applying syntactic pre-ordering to statistical machine translation is effective especially for language pairs with different word orders, however; current methods require a supervised syntactic parser.
Approach: they propose an unsupervised method that can build parsers for pre-ordering using a word-aligned parallel corpus without syntactic annotations.
Outcome: The pre-ordering parsers obtained by the proposed method can perform better reordering than state-of-the-art methods and are also effective with machine translation systems.