In this paper, we build our model based on the powerful Transformer (Vaswani et al., 2017) with an encoder-decoder framework, where the encoder network first transforms an input sequence of symbols x = (x 1 , x 2 , ..., x n ) to a sequence of continues representations z = (z 1 , z 2 , ..., z n ), from which the decoder generates an output sequence y = (y 1 , y 2 , ..., y m ) one element at a time.
relying entirely on the multi-head attention mechanism, the Transformer with beam search algorithm achieves the state-of-the-art results for machine translation.