Word's vector representations meet machine translation

Abstract

Distributed vector representations of words are useful in various NLP tasks. We briefly review the CBOW approach and propose a bilingual application of this architecture with the aim to improve consistency and coherence of Machine Translation. The primary goal of the bilingual extension is to handle ambiguous words for which the different senses are conflated in the monolingual setup.