In the training phase, given a parallel sentence (x, y), NMT models the conditional probability as follows, where y i is the target word emitted by the decoder at step i and y <i = y 1 , y 2 , ..., y i−1 .
to explore the phrase generation in NMt beyond the word-byword generation framework, we propose a novel architecture that integrates a phrase-based SMt model into NMt.