Integrating Prior Translation Knowledge into Neural Machine Translation

Neural machine translation (NMT), which is an encoder-decoder joint neural language model with an attention mechanism, has achieved impressive results on various machine translation tasks in the past several years. However, the language model attribute of NMT tends to produce fluent yet sometimes unfaithful translations, which hinders the improvement of translation capacity. In response to this problem, we propose a simple and efficient method to integrate prior translation knowledge into NMT in a universal manner that is compatible with neural networks. Meanwhile, it enables NMT to consider the crossing language translation knowledge from the source-side of the training pipeline of NMT, thereby making full use of the prior translation knowledge to enhance the performance of NMT. The experimental results on two large-scale benchmark translation tasks demonstrated that our approach achieved a significant improvement over a strong baseline.