This paper proposes a method for learning a discriminative parser for machine translation reordering using only aligned parallel text. This is done by treating the parser's derivation tree as a latent variable in a model that is trained to maximize reordering accuracy. We demonstrate that efficient large-margin training is possible by showing that two measures of reordering 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 phrasebased SMT and previously proposed unsupervised syntax induction methods.
REF follow a similar approach but build a single ITG-style pre-ordering model treating the parse tree as a latent variable.
Inducing a Discriminative Parser to Optimize Machine Translation Reordering
EMNLP
Computer Science