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 ChineseEnglish translation show that our approach leads to significant improvements. 1
REF integrated multiple prior knowledge sources into neural machine translation using posterior regularization.
Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
ArXiv
ArXiv
Computer Science