  Neural machine translation (NMT) has recently gained widespread attention
because of its high translation accuracy. However, it shows poor performance in
the translation of long sentences, which is a major issue in low-resource
languages. It is assumed that this issue is caused by insufficient number of
long sentences in the training data. Therefore, this study proposes a simple
data augmentation method to handle long sentences. In this method, we use only
the given parallel corpora as the training data and generate long sentences by
concatenating two sentences. Based on the experimental results, we confirm
improvements in long sentence translation by the proposed data augmentation
method, despite its simplicity. Moreover, the translation quality is further
improved by the proposed method, when combined with back-translation.
