This paper describes a recently developed method for computing a consensus translation from the outputs of multiple machine translation (MT) systems. A possibly new translation hypothesis can be produced as a result of this system combination algorithm. The consensus translation is computed by creating a confusion network and performing weighted majority voting, similarly to the well-established ROVER approach of (Fiscus 1997) for combining speech recognition hypotheses. To create the confusion network, pairwise word alignments of the original machine translation hypotheses are learned by using an enhanced statistical alignment algorithm that explicitly models word reordering. This is the first known application of this algorithm in the context of system combination. The context of a whole document of translations rather than a single sentence is taken into account to improve the alignment quality. The proposed alignment and voting approach was evaluated on several machine translation tasks, including a large vocabulary task. The method was also tested in the framework of multi- source and speech translation. Significant improvements in translation quality were achieved on all tasks. Here, we report experimental results for combining MT systems participating in the TC-STAR (speech translation) Project.

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