A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder’s job is to find the translation that is most likely according to set of previously learned parameters (and a formula for combin- ing them). Since the space of possi- ble translations is extremely large, typ- ical decoding algorithms are only able to examine a portion of it, thus risk- ing to miss good solutions. In this pa- per, we compare the speed and out- put quality of a traditional stack-based decoding algorithm with two new de- coders: a fast greedy decoder and a slow but optimal decoder that treats de- coding as an integer-programming opti- mization problem.

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