Low cost portability for statistical machine translation based on n-gram frequency and TF-IDF

Statistical machine translation relies heavily on the available
training data. In some cases it is necessary to limit the amount
of training data that can be created for or actually used by the
systems. We introduce weighting schemes which allow us to
sort sentences based on the frequency of unseen n-grams. A
second approach uses TF-IDF to rank the sentences. After
sorting we can select smaller training corpora and we are able
to show that systems trained on much less training data
achieve a very competitive performance compared to baseline
systems using all available training data.