The approach we take in this article is motivated by the fact that natural language allows for multiple text views on a given content, and that if two phrases are good paraphrases in context, then considering appropriate training examples of one of the phrases could provide larger quantities of training data for translating the other.
we hypothesize that there may be more training data to learn a phrase's translations in a bilingual corpus than what SMT approaches typically use.