By automatically annotating unlabeled bitexts with these bilingual models, we can train new monolingual models that do not rely on bilingual data at test time, but still perform substantially better than models trained using only monolingual resources.
we are training the bilingual view to match the output of these same models, which can be trivially achieved by putting weight on only the monolingual model scores and never recruiting any bilingual features.