Task-Level Curriculum Learning for Non-Autoregressive Neural Machine Translation
Table 6: The comparison of BLEU scores on the test set of IWSLT14 De-En task among different task windows.
['[BOLD] Task Window', '[ITALIC] w=1', '[ITALIC] w=2', '[ITALIC] w=3', '[ITALIC] w=4']
S4SS3SSS0Px3 Analysis on Task Window. From the table, we can see that the model trained with w=2 achieves the best score in IWSLT14 De-En task, which proves that an appropriate task window w can help reduce the gap between neighboring stages, and thus help model training.