Paper: Inducing a Discriminative Parser to Optimize Machine Translation Reordering

ACL ID D12-1077
Title Inducing a Discriminative Parser to Optimize Machine Translation Reordering
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

This paper proposes a method for learning a discriminative parser for machine trans- lation reordering using only aligned par- allel text. This is done by treating the parser?s derivation tree as a latent variable in a model that is trained to maximize re- ordering accuracy. We demonstrate that efficient large-margin training is possible by showing that two measures of reorder- ing accuracy can be factored over the parse tree. Using this model in the pre-ordering framework results in significant gains in translation accuracy over standard phrase- based SMT and previously proposed unsu- pervised syntax induction methods.