Multilingual Denoising Pre-training for Neural Machine Translation
Table 11: Unsupervised MT via Language Transfer on X-En translations. The model fine-tuned on one language pair is directly tested on another. We use gray color to show the direct fine-tuning results, and lightgray color to show language transfer within similar language groups. We bold the highest transferring score for each pair.
['[EMPTY]', '[EMPTY]', '[BOLD] Fine-tuning Languages  [BOLD] Zh', '[BOLD] Fine-tuning Languages  [BOLD] Ja', '[BOLD] Fine-tuning Languages  [BOLD] Ko', '[BOLD] Fine-tuning Languages  [BOLD] Cs', '[BOLD] Fine-tuning Languages  [BOLD] Ro', '[BOLD] Fine-tuning Languages  [BOLD] Nl', '[BOLD] Fine-tuning Languages  [BOLD] It', '[BOLD] Fine-tuning Languages  [BOLD] Ar', '[BOLD] Fine-tuning Languages  [BOLD] Hi', '[BOLD] Fine-tuning Languages  [BOLD] Ne', '[BOLD] Fine-tuning Languages  [BOLD] Si', '[BOLD] Fine-tuning Languages  [BOLD] Gu']
We can always obtain reasonable transferring scores at all pairs over different fine-tuned models except from Gu-En where the supervised model completely fails (0.3 BLEU). In some cases, we can achieve similar (Cs-En) or even much better (Ne-En, Gu-En) results compared to the supervised results.