Papers › Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization

Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization

2 Nov 2018ACL 2017 7arXiv:1811.01100archive 2025-07-28

Jiacheng Zhang, Yang Liu, Huanbo Luan, Jingfang Xu, Maosong Sun

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.

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