  Neural Machine Translation (NMT) has become the new state-of-the-art in
several language pairs. However, it remains a challenging problem how to
integrate NMT with a bilingual dictionary which mainly contains words rarely or
never seen in the bilingual training data. In this paper, we propose two
methods to bridge NMT and the bilingual dictionaries. The core idea behind is
to design novel models that transform the bilingual dictionaries into adequate
sentence pairs, so that NMT can distil latent bilingual mappings from the ample
and repetitive phenomena. One method leverages a mixed word/character model and
the other attempts at synthesizing parallel sentences guaranteeing massive
occurrence of the translation lexicon. Extensive experiments demonstrate that
the proposed methods can remarkably improve the translation quality, and most
of the rare words in the test sentences can obtain correct translations if they
are covered by the dictionary.
