Current SMT systems usually decode with single translation models and cannot ben- efit from the strengths of other models in decoding phase. We instead propose joint decoding, a method that combines multi- ple translation models in one decoder. Our joint decoder draws connections among multiple models by integrating the trans- lation hypergraphs they produce individu- ally. Therefore, one model can share trans- lations and even derivations with other models. Comparable to the state-of-the-art system combination technique, joint de- coding achieves an absolute improvement of 1.5 BLEU points over individual decod- ing.

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