The reason of not using all the k-best results for improving SMT is that the top-k paraphrases generated for a sentence are generally very similar, if we train the SMT model on all these sentences, it would be quite time-consuming and much of the computation is vain.
we propose an algorithm to select a subset from all the paraphrase sentences, which can cover most of the newly introduced information while dramatically reduce the numbers of paraphrases.