  Neural machine translation systems require a number of stacked layers for
deep models. But the prediction depends on the sentence representation of the
top-most layer with no access to low-level representations. This makes it more
difficult to train the model and poses a risk of information loss to
prediction. In this paper, we propose a multi-layer representation fusion
(MLRF) approach to fusing stacked layers. In particular, we design three fusion
functions to learn a better representation from the stack. Experimental results
show that our approach yields improvements of 0.92 and 0.56 BLEU points over
the strong Transformer baseline on IWSLT German-English and NIST
Chinese-English MT tasks respectively. The result is new state-of-the-art in
German-English translation.
