Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
Table 2: Translation results of different transfer learning setups.
['System', 'Bleu [%] eu-en', 'Bleu [%] sl-en', 'Bleu [%] be-en', 'Bleu [%] az-en', 'Bleu [%] tr-en']
Plain transfer learning already gives a boost but is still far from a satisfying quality, especially for Basque→-English and Azerbaijani→English. On top of that, each of our three techniques offers clear, incremental improvements in all child language pairs with a maximum of 5.1% Bleu in total. Such tokens play the role of anchors in translation; they are sure to be copied to the target side. The surrounding   tokens are spread according to the source language structure, whereas merely copying the target sentence to the source Currey et al. Note that our trick applies also to the languages with completely different alphabets, e.g. Belarusian and German