Translating with non-contiguous phrases 
 This paper presents a phrase-based statistical machine translation method , based on non-contiguous phrases , i.e. phrases with gaps. A method for producing such phrases from a word-aligned corpora is proposed. A statistical translation model is also presented that deals such phrases , as well as a training method based on the maximization of translation accuracy , as measured with the NIST evaluation metric . Translations are produced by means of a beam-search decoder . Experimental results are presented, that demonstrate how the proposed method allows to better generalize from the training data . 