This paper studies three techniques that improve the quality of N-best hypotheses through additional regeneration process. Unlike the multi-system consensus ap- proach where multiple translation sys- tems are used, our improvement is achieved through the expansion of the N- best hypotheses from a single system. We explore three different methods to im- plement the regeneration process: re- decoding, n-gram expansion, and confu- sion network-based regeneration. Ex- periments on Chinese-to-English NIST and IWSLT tasks show that all three methods obtain consistent improvements. Moreover, the combination of the three strategies achieves further improvements and outperforms the baseline by 0.81 BLEU-score on IWSLT’06, 0.57 on NIST’03, 0.61 on NIST’05 test set re- spectively. ...

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