Balancing Training for Multilingual Neural Machine Translation
Table 3: Average BLEU of the best baseline and three MultiDDS-S settings for the Diverse group. MultiDDS-S always outperform the baseline.
['[BOLD] Setting', '[BOLD] Baseline', '[BOLD] MultiDDS-S  [BOLD] Regular', '[BOLD] MultiDDS-S  [BOLD] Low', '[BOLD] MultiDDS-S  [BOLD] High']
We performed experiments with these aggregation methods on the Diverse group, mainly because there is more performance trade-off among these languages. The languages are ordered on the x-axis from left to right in decreasing perplexity. Low generally performs better on the low-performing languages on the left, while High generally achieves the best performance on the high-performing languages on the right, with results most consistent in the O2M setting. This indicates that MultiDDS is able to prioritize different predefined objectives.