CUED Publications database

Source-side preordering for translation using logistic regression and depth-first branch-And-bound search

Jehl, L and De Gispert, A and Hopkins, M and Byrne, W (2014) Source-side preordering for translation using logistic regression and depth-first branch-And-bound search. In: UNSPECIFIED pp. 239-248..

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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. © 2014 Association for Computational Linguistics.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:05
Last Modified: 26 Oct 2017 01:48
DOI: