  Recurrent Neural Networks have long been the dominating choice for sequence
modeling. However, it severely suffers from two issues: impotent in capturing
very long-term dependencies and unable to parallelize the sequential
computation procedure. Therefore, many non-recurrent sequence models that are
built on convolution and attention operations have been proposed recently.
Notably, models with multi-head attention such as Transformer have demonstrated
extreme effectiveness in capturing long-term dependencies in a variety of
sequence modeling tasks. Despite their success, however, these models lack
necessary components to model local structures in sequences and heavily rely on
position embeddings that have limited effects and require a considerable amount
of design efforts. In this paper, we propose the R-Transformer which enjoys the
advantages of both RNNs and the multi-head attention mechanism while avoids
their respective drawbacks. The proposed model can effectively capture both
local structures and global long-term dependencies in sequences without any use
of position embeddings. We evaluate R-Transformer through extensive experiments
with data from a wide range of domains and the empirical results show that
R-Transformer outperforms the state-of-the-art methods by a large margin in
most of the tasks. We have made the code publicly available at
\url{https://github.com/DSE-MSU/R-transformer}.
