  In this paper, we investigate the driving factors behind concatenation, a
simple but effective data augmentation method for low-resource neural machine
translation. Our experiments suggest that discourse context is unlikely the
cause for the improvement of about +1 BLEU across four language pairs. Instead,
we demonstrate that the improvement comes from three other factors unrelated to
discourse: context diversity, length diversity, and (to a lesser extent)
position shifting.
