  Recent years has witnessed dramatic progress of neural machine translation
(NMT), however, the method of manually guiding the translation procedure
remains to be better explored. Previous works proposed to handle such problem
through lexcially-constrained beam search in the decoding phase. Unfortunately,
these lexically-constrained beam search methods suffer two fatal disadvantages:
high computational complexity and hard beam search which generates unexpected
translations. In this paper, we propose to learn the ability of
lexically-constrained translation with external memory, which can overcome the
above mentioned disadvantages. For the training process, automatically
extracted phrase pairs are extracted from alignment and sentence parsing, then
further be encoded into an external memory. This memory is then used to provide
lexically-constrained information for training through a memory-attention
machanism. Various experiments are conducted on WMT Chinese to English and
English to German tasks. All the results can demonstrate the effectiveness of
our method.
