Translating with Non-contiguous Phrases

Michel Simard, Nicola Cancedda, Bruno Cavestro, Marc Dymetman, Éric Gaussier, Cyril Goutte, Kenji Yamada, Philippe Langlais, Arne Mauser

Abstract: 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.