Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation
Table 1: German→English translation quality (Bleu) of systems with different ratios of real:syn data.
['[EMPTY]', '[BOLD] Size', '[BOLD] 2014', '[BOLD] 2015', '[BOLD] 2016', '[BOLD] 2017']
Comparing the systems using ratios of (1:1) and (1:4), we see that the latter achieves lower perplexity during training. The Bleu scores show that the translation quality does not improve linearly with the size of the synthetic data. The model trained on (1:4) real to synthetic ratio of training data achieves the best results, however, the performance is close to the model trained on (1:1) training data.