1. A method for training a translation scoring model for a statistical machine translation system, comprising: receiving a set of source sentences, each of the source sentences comprising source words in a source language; for each source sentence, generating a target sentence comprising target words in a target language which are translations of each of the source words in the source sentence; after generating the target sentence, generating a plurality of translation neighbors based on the target sentence, each translation neighbor comprising at least some of the target words of the target sentence; for each of the plurality of translation neighbors, computing a phrase alignment between the source sentence and the translation neighbor; scoring each translation neighbor with a translation scoring model, based on the computed phrase alignment between the source sentence and the translation neighbor; ranking a plurality of the translation neighbors based on the translation model scores; updating parameters of the translation scoring model based on a comparison of the ranking based on the translation model scores with an external ranking of the plurality of translation neighbors; and iterating, at least once more, the generating of translation neighbors, scoring, ranking, and updating the parameters of the translation scoring model, wherein in the generating of the plurality of translation neighbors of the target sentence, the target sentence is a translation neighbor from a prior iteration; wherein at least one of the generating of translation neighbors, scoring, ranking, and updating parameters is performed with a computer processor.
1. A method for training a translation scoring model for a statistical machine translation system, comprising: receiving a set of source sentences, each of the source sentences comprising source words in a source language; for each source sentence, generating a target sentence comprising target words in a target language which are translations of each of the source words in the source sentence; after generating the target sentence, generating a plurality of translation neighbors based on the target sentence, each translation neighbor comprising at least some of the target words of the target sentence; for each of the plurality of translation neighbors, computing a phrase alignment between the source sentence and the translation neighbor; scoring each translation neighbor with a translation scoring model, based on the computed phrase alignment between the source sentence and the translation neighbor; ranking a plurality of the translation neighbors based on the translation model scores; updating parameters of the translation scoring model based on a comparison of the ranking based on the translation model scores with an external ranking of the plurality of translation neighbors; and iterating, at least once more, the generating of translation neighbors, scoring, ranking, and updating the parameters of the translation scoring model, wherein in the generating of the plurality of translation neighbors of the target sentence, the target sentence is a translation neighbor from a prior iteration; wherein at least one of the generating of translation neighbors, scoring, ranking, and updating parameters is performed with a computer processor. 5. The method of claim 1 , wherein the parameters are feature weights of a log-linear probabilistic model of the form P(t|s)∝exp(Σ k Θ k φ k (s, t)) and wherein the translation scoring model outputs a score for a translation neighbor which is a function of the expression exp(Σ k Θ k φ k (s, t)), where each φ k (s, t) represents a feature which is a function of the source sentence s and the translation neighbor t and Θ represents a vector of the feature weights indexed by k.