  Although neural machine translation has made significant progress recently,
how to integrate multiple overlapping, arbitrary prior knowledge sources
remains a challenge. In this work, we propose to use posterior regularization
to provide a general framework for integrating prior knowledge into neural
machine translation. We represent prior knowledge sources as features in a
log-linear model, which guides the learning process of the neural translation
model. Experiments on Chinese-English translation show that our approach leads
to significant improvements.
