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

ACL ID E14-1026
Title Source-side Preordering for Translation using Logistic Regression and Depth-first Branch-and-Bound Search
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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 chil- dren regression scores we conduct an effi- cient depth-first branch-and-bound search through the space of possible children per- mutations, avoiding using a cascade of classifiers or limiting the list of possi- ble ordering outcomes. We report exper- iments in translating English to Japanese and Korean, demonstrating superior per- formance as (a) the number of crossing links drops by more than 10% absolute with respect to other state-of-the-art pre- ordering approaches, (b) BLEU scores im- prove on 2.2 points over the baseline with lexicalised reo...