Balancing Training for Multilingual Neural Machine Translation
Table 1: Average BLEU for the baselines and our methods. Bold indicates the highest value.
['[EMPTY]', '[BOLD] Method', '[BOLD] M2O  [BOLD] Related', '[BOLD] M2O  [BOLD] Diverse', '[BOLD] O2M  [BOLD] Related', '[BOLD] O2M  [BOLD] Diverse']
First, comparing the baselines, we can see that there is no consistently strong strategy for setting the sampling ratio, with proportional sampling being best in the M2O setting, but worst in the O2M setting. Next, we can see that MultiDDS outperforms the best baseline in three of the four settings and is comparable to proportional sampling in the last M2O-Diverse setting. With the stabilized reward, MultiDDS-S consistently delivers better overall performance than the best baseline, and outperforms MultiDDS in three settings. From these results, we can conclude that MultiDDS-S provides a stable strategy to train multilingual systems over a variety of settings.