Although attention-based Neural Machine Translation (NMT) has achieved
remarkable progress in recent years, it still suffers from issues of repeating
and dropping translations. To alleviate these issues, we propose a novel
key-value memory-augmented attention model for NMT, called KVMEMATT.
Specifically, we maintain a timely updated keymemory to keep track of attention
history and a fixed value-memory to store the representation of source sentence
throughout the whole translation process. Via nontrivial transformations and
iterative interactions between the two memories, the decoder focuses on more
appropriate source word(s) for predicting the next target word at each decoding
step, therefore can improve the adequacy of translations. Experimental results
on Chinese=>English and WMT17 German<=>English translation tasks demonstrate
the superiority of the proposed model.

提出了一种基于键值记忆的注意力机制模型用于神经机器翻译，通过维护及时更新的键内存来跟踪注意力历史和固定值内存来存储源语句的表示，在两个记忆之间进行非平凡的转换和迭代交互，以便每个解码步骤时，可以关注更合适的源单词来预测下一个目标单词从而提高翻译的适用性。在中英文和 WMT17 德英翻译任务的实验结果表明了所提出的模型的优越性。