Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation
Table 10: Results using back-translation. ✓† indicates the use of a low-quality back-translation made with a non-multilingual non-autoencoder vanilla BT model.
['Method', 'ML', 'BT', 'Autoencoder SRC', 'Autoencoder HRL', 'Autoencoder LRL', 'eng–est chrF1', 'eng–est BLEU', 'eng–est rare', 'eng–dan chrF1', 'eng–dan BLEU', 'eng–dan rare', 'eng–slo chrF1', 'eng–slo BLEU', 'eng–slo rare']
We weight the natural and synthetic LRL data equally. Back-translation is generally effective, giving a benefit between +1.31 and +4.46 BLEU. When using back-translated data, the autoencoder task is less effective, with small improvements to Character F1 but inconsistent results for the other measures. Note that back-translation is not a silver bullet. When using only back-translation, i.e. back-translating the data with a weak model trained only on the low-resource parallel data, and then training a forward model augmented only by this low-quality back-translation but not multilingual training or autoencoder, the performance is very low: only +2.87 BLEU better than the vanilla model without back-translation. The high-quality back-translation together with multilingual training gives an +12.7 BLEU increase over the vanilla back-translation.