  Context modeling is essential to generate coherent and consistent translation
for Document-level Neural Machine Translations. The widely used method for
document-level translation usually compresses the context information into a
representation via hierarchical attention networks. However, this method
neither considers the relationship between context words nor distinguishes the
roles of context words. To address this problem, we propose a query-guided
capsule networks to cluster context information into different perspectives
from which the target translation may concern. Experiment results show that our
method can significantly outperform strong baselines on multiple data sets of
different domains.
