  In this study, we first investigate a novel capsule network with dynamic
routing for linear time Neural Machine Translation (NMT), referred as
\textsc{CapsNMT}. \textsc{CapsNMT} uses an aggregation mechanism to map the
source sentence into a matrix with pre-determined size, and then applys a deep
LSTM network to decode the target sequence from the source representation.
Unlike the previous work \cite{sutskever2014sequence} to store the source
sentence with a passive and bottom-up way, the dynamic routing policy encodes
the source sentence with an iterative process to decide the credit attribution
between nodes from lower and higher layers. \textsc{CapsNMT} has two core
properties: it runs in time that is linear in the length of the sequences and
provides a more flexible way to select, represent and aggregates the part-whole
information of the source sentence. On WMT14 English-German task and a larger
WMT14 English-French task, \textsc{CapsNMT} achieves comparable results with
the state-of-the-art NMT systems. To the best of our knowledge, this is the
first work that capsule networks have been empirically investigated for
sequence to sequence problems.
