Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution
Toan Quang Nguyen, Kenton Murray, David Chiang · 2021
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 concatenation improving BLEU by about +1 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.