  Although attention-based Neural Machine Translation has achieved remarkable
progress in recent layers, it still suffers from issue of making insufficient
use of the output of each layer. In transformer, it only uses the top layer of
encoder and decoder in the subsequent process, which makes it impossible to
take advantage of the useful information in other layers. To address this
issue, we propose a residual tree aggregation of layers for Transformer(RTAL),
which helps to fuse information across layers. Specifically, we try to fuse the
information across layers by constructing a post-order binary tree. In
additional to the last node, we add the residual connection to the process of
generating child nodes. Our model is based on the Neural Machine Translation
model Transformer and we conduct our experiments on WMT14 English-to-German and
WMT17 English-to-France translation tasks. Experimental results across language
pairs show that the proposed approach outperforms the strong baseline model
significantly
