SwitchOut: an Efficient Data Augmentation Algorithmfor Neural Machine Translation
Table 1: Test BLEU scores of SwitchOut and other baselines (median of multiple runs). Results marked with † are statistically significant compared to the best result without SwitchOut. For example, for en-de results in the first column, +SwitchOut has significant gain over Transformer; +RAML +SwitchOut has significant gain over +RAML.
['[BOLD] Method', '[BOLD] en-de', '[BOLD] de-en', '[BOLD] en-vi']
To account for variance, we run each experiment multiple times and report the median BLEU. Specifically, each experiment without SwitchOut is run for 4 times, while each experiment with SwitchOut is run for 9 times due to its inherently higher variance. For 4 of the 6 settings, SwitchOut delivers significant improvements over the best baseline without SwitchOut. For the remaining two settings, the differences are not statistically significant. The gains in BLEU with SwitchOut over the best baseline on WMT 15 en-de are all significant (p<0.0002). Notably, SwitchOut on the source demonstrates as large gains as these obtained by RAML on the target side, and SwitchOut delivers further improvements when combined with RAML.