We present a simple preordering approach for machine translation based on a featurerich logistic regression model to predict whether two children of the same node in the source-side parse tree should be swapped or not . Given the pair-wise children regression scores we conduct an efficient depth-first branch-and-bound search through the space of possible children permutations , avoiding using a cascade of classifiers or limiting the list of possible ordering outcomes . We report experiments in translating English to Japanese and Korean , demonstrating superior performance as ( a ) the number of crossing links drops by more than 10 % absolute with respect to other state-of-the-art preordering approaches , ( b ) BLEU scores improve on 2.2 points over the baseline with lexicalised reordering model , and ( c ) decoding can be carried out 80 times faster . Source-side preordering for translation is the task of rearranging the order of a given source sentence so that it best resembles the order of the target sentence . It is a divide-and-conquer strategy aiming to decouple long-range word movement from the core translation task . The main advantage is that translation becomes computationally cheaper as less word movement needs to be considered , which results in faster and better translations , if preordering is done well and efficiently . Preordering also can facilitate better estimation of alignment and translation models as the parallel data becomes more monotonically-aligned , and translation gains can be obtained for various system architectures , e.g. phrase-based , hierarchical phrase-based , etc . For these reasons , preordering has a clear research and commercial interest , as reflected by the extensive previous work on the subject ( see Section 2 ) . From these approaches , we are particularly interested in those that ( i ) involve little or no human intervention , ( ii ) require limited computational resources at runtime , and ( iii ) make use of available linguistic analysis tools . In this paper we propose a novel preordering approach based on a logistic regression model trained to predict whether to swap nodes in the source-side dependency tree . For each pair of sibling nodes in the tree , the model uses a feature-rich representation that includes lexical cues to make relative reordering predictions between them . Given these predictions , we conduct a depth-first branch-and-bound search through the space of possible permutations of all sibling nodes , using the regression scores to guide the search . This approach has multiple advantages . First , the search for permutations is efficient and does not require specific heuristics or hard limits for nodes with many children . Second , the inclusion of the regression prediction directly into the search allows for finer-grained global decisions as the predictions that the model is more confident about are preferred . Finally , the use of a single regression model to handle any number of child nodes avoids incurring sparsity issues , while allowing the integration of a vast number of features into the preordering model . We empirically contrast our proposed method against another preordering approach based on automatically-extracted rules when translating English into Japanese and Korean . We demonstrate a significant reduction in number of crossing links of more than 10 % absolute , as well as translation gains of over 2.2 BLEU points over the baseline . We also show it outperforms a multi-class classification approach and analyse why this is the case . We have presented a novel preordering approach that estimates a preference for swapping or not swapping pairs of children nodes in the sourceside dependency tree by training a feature-rich logistic regression model . Given the pair-wise scores , we efficiently search through the space of possible children permutations using depth-first branch-and-bound search . The approach is able to incorporate large numbers of features including lexical cues , is efficient at runtime even with a large number of children , and proves superior to other state-of-the-art preordering approaches both in terms of crossing score and translation performance .

Challenge: Preordering methods for machine translation systems that involve little or no human assistance, run on limited computational resources and use linguistic analysis tools are required.
Approach: They propose a logistic regression-based model with lexical features which predicts whether two children of the same node in the parse tree should be swapped.
Outcome: Experiments on translation tasks from English to Japanese and Korean show the proposed method outperforms baseline preordering methods and runs 80 times faster.