  Among the six challenges of neural machine translation (NMT) coined by (Koehn
and Knowles, 2017), rare-word problem is considered the most severe one,
especially in translation of low-resource languages. In this paper, we propose
three solutions to address the rare words in neural machine translation
systems. First, we enhance source context to predict the target words by
connecting directly the source embeddings to the output of the attention
component in NMT. Second, we propose an algorithm to learn morphology of
unknown words for English in supervised way in order to minimize the adverse
effect of rare-word problem. Finally, we exploit synonymous relation from the
WordNet to overcome out-of-vocabulary (OOV) problem of NMT. We evaluate our
approaches on two low-resource language pairs: English-Vietnamese and
Japanese-Vietnamese. In our experiments, we have achieved significant
improvements of up to roughly +1.0 BLEU points in both language pairs.
