Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation
Table 5: Results for cross-lingual transfer. Abbreviations: ML for multilingual, BT for back-translation, AE for autoencoder.
['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']
The cross-lingual transfer from the high-resource language yields the largest single improvement in our experiments. The multilingual model without autoencoder performs between +10.26 and +12.7 BLEU better than the vanilla model using only LRL parallel data. Adding an autoencoder loss results in a smaller gain, between +4.97 and +5.55 BLEU. The gains are partly cumulative for an additional gain of +0.05 to +1.14 BLEU.