Source-side Preordering for Translation using Logistic Regression and Depth-first Branch-and-Bound Search

Laura Jehl, Adrià de Gispert, Mark Hopkins, Bill Byrne
2014 Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics  
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
more » ... 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. * This work was done during an internship of the first author at SDL Research, Cambridge.
doi:10.3115/v1/e14-1026 dblp:conf/eacl/JehlGHB14 fatcat:jnke4bvnvbamhcxbnv2vzbmgyq