  In machine translation, we often try to collect resources to improve
performance. However, most of the language pairs, such as Korean-Arabic and
Korean-Vietnamese, do not have enough resources to train machine translation
systems. In this paper, we propose the use of synthetic methods for extending a
low-resource corpus and apply it to a multi-source neural machine translation
model. We showed the improvement of machine translation performance through
corpus extension using the synthetic method. We specifically focused on how to
create source sentences that can make better target sentences, including the
use of synthetic methods. We found that the corpus extension could also improve
the performance of multi-source neural machine translation. We showed the
corpus extension and multi-source model to be efficient methods for a
low-resource language pair. Furthermore, when both methods were used together,
we found better machine translation performance.
