Vector Space Models for Phrase-based Machine Translation

This paper investigates the application of vector space models (VSMs) to the standard phrase-based machine translation pipeline. VSMs are models based on continuous word representations embed-ded in a vector space. We exploit word vectors to augment the phrase table with new inferred phrase pairs. This helps reduce out-of-vocabulary (OOV) words. In addition, we present a simple way to learn bilingually-constrained phrase vec-tors. The phrase vectors are then used to provide additional scoring of phrase pairs, which fits into the standard log-linear framework of phrase-based statistical ma-chine translation. Both methods result in significant improvements over a com-petitive in-domain baseline applied to the Arabic-to-English task of IWSLT 2013.