9. A computer program embodied on a non-transitory computer readable medium having instructions for aligning words in parallel segments comprising: calculating a first probability distribution of word alignments within a first corpus comprising unaligned parallel segments according to a model estimate, the model estimate comprising an N-best list of one or more sub-models; modifying the model estimate according to the probability distribution; discriminatively re-ranking one or more sub-models within the modified model estimate according to annotated parallel segments; and calculating a second probability distribution of the word alignments within the first corpus according to the re-ranked modified model estimate; wherein discriminatively re-ranking one or more sub-models within the modified model estimate according to manual alignments further comprises: adding manual alignments to hypothesized alignments within the first corpus; comparing the manual alignments to the hypothesized alignments; and weighting the one or more sub-models according to the comparison; wherein: the weighting of the one or more sub-models according to the comparison is according to at least one weighting factor; and the discriminative re-ranking of the one or more sub-models within the modified model estimate according to manual alignments further comprises refining at least one of the at least one weighting factors using a one-dimensional error minimization until there is no further error reduction; and wherein the refining of the at least one weighting factor further comprises calculating a piecewise constant function that evaluates an error of the word alignments selected by a best word alignment equation keeping the at least one weighting factor for each of the one or more sub-models constant except for one of the at least one weighting factor for the sub-model being evaluated.
9. A computer program embodied on a non-transitory computer readable medium having instructions for aligning words in parallel segments comprising: calculating a first probability distribution of word alignments within a first corpus comprising unaligned parallel segments according to a model estimate, the model estimate comprising an N-best list of one or more sub-models; modifying the model estimate according to the probability distribution; discriminatively re-ranking one or more sub-models within the modified model estimate according to annotated parallel segments; and calculating a second probability distribution of the word alignments within the first corpus according to the re-ranked modified model estimate; wherein discriminatively re-ranking one or more sub-models within the modified model estimate according to manual alignments further comprises: adding manual alignments to hypothesized alignments within the first corpus; comparing the manual alignments to the hypothesized alignments; and weighting the one or more sub-models according to the comparison; wherein: the weighting of the one or more sub-models according to the comparison is according to at least one weighting factor; and the discriminative re-ranking of the one or more sub-models within the modified model estimate according to manual alignments further comprises refining at least one of the at least one weighting factors using a one-dimensional error minimization until there is no further error reduction; and wherein the refining of the at least one weighting factor further comprises calculating a piecewise constant function that evaluates an error of the word alignments selected by a best word alignment equation keeping the at least one weighting factor for each of the one or more sub-models constant except for one of the at least one weighting factor for the sub-model being evaluated. 14. The computer program recited in claim 9 , further comprising an instruction for initializing the model estimate.