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

Source-side Preordering for Translation using Logistic Regression and Depth-first Branch-and-Bound Search” by Laura Jehl, Adrià de Gispert, Mark Hopkins, and Bill Byrne. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, (Gothenburg, Sweden), Apr. 2014, pp. 239-248 (12 pages).

Abstract

We present a simple preordering approach for machine translation based on a feature-rich 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.

BibTeX entry:

@inproceedings{jehl-EtAl:2014:EACL,
   author = {Laura Jehl and Adri{\`a} de Gispert and Mark Hopkins and Bill
	Byrne},
   title = {Source-side Preordering for Translation using Logistic
	Regression and Depth-first Branch-and-Bound Search},
   booktitle = {Proceedings of the 14th Conference of the European Chapter
	of the Association for Computational Linguistics},
   pages = {239--248 (12 pages)},
   publisher = {Association for Computational Linguistics},
   address = {Gothenburg, Sweden},
   month = apr,
   year = {2014},
   url = {http://www.aclweb.org/anthology/E14-1026}
}

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