  In this paper, we enhance the attention-based neural machine translation
(NMT) by adding explicit coverage embedding models to alleviate issues of
repeating and dropping translations in NMT. For each source word, our model
starts with a full coverage embedding vector to track the coverage status, and
then keeps updating it with neural networks as the translation goes.
Experiments on the large-scale Chinese-to-English task show that our enhanced
model improves the translation quality significantly on various test sets over
the strong large vocabulary NMT system.
