  South and North Korea both use the Korean language. However, Korean NLP
research has focused on South Korean only, and existing NLP systems of the
Korean language, such as neural machine translation (NMT) models, cannot
properly handle North Korean inputs. Training a model using North Korean data
is the most straightforward approach to solving this problem, but there is
insufficient data to train NMT models. In this study, we create data for North
Korean NMT models using a comparable corpus. First, we manually create
evaluation data for automatic alignment and machine translation. Then, we
investigate automatic alignment methods suitable for North Korean. Finally, we
verify that a model trained by North Korean bilingual data without human
annotation can significantly boost North Korean translation accuracy compared
to existing South Korean models in zero-shot settings.
