Title: Information-Propogation-Enhanced Neural Machine Translation by Relation Model,
Abstract: Even though sequence-to-sequence neural machine translation (NMT) model have
achieved state-of-art performance in the recent fewer years, but it is widely
concerned that the recurrent neural network (RNN) units are very hard to
capture the long-distance state information, which means RNN can hardly find
the feature with long term dependency as the sequence becomes longer.
Similarly, convolutional neural network (CNN) is introduced into NMT for
speeding recently, however, CNN focus on capturing the local feature of the
sequence; To relieve this issue, we incorporate a relation network into the
standard encoder-decoder framework to enhance information-propogation in neural
network, ensuring that the information of the source sentence can flow into the
decoder adequately. Experiments show that proposed framework outperforms the
statistical MT model and the state-of-art NMT model significantly on two data
sets with different scales.