Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
This work addresses a specific problem in machine translation for researchers and practitioners, offering an incremental method to enhance translation quality.
The paper tackles the challenge of integrating multiple overlapping prior knowledge sources into neural machine translation by proposing a posterior regularization framework, resulting in significant improvements on Chinese-English translation.
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.