Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation
TABLE V: Out-of-domain results (BLEU-4 scores) after domain adaptation with one out-of-domain corpus for ALT-JE using KFTT-JE, IWSLT-JE and IWSLT-CE. The numbers in bold indicate the best scores using the same data. The numbers marked with “†” indicate that the results using our proposed methods are significantly better (p <0.05) than the existing black-box methods for the same data setting. The numbers marked with “‡” indicate the best score over all data settings. Note that fine tuning was conducted on the in-domain data only and thus using this in-domain model to translate out-of-domain/language data shows significantly low performance.
['[BOLD] No.', '[BOLD] KFTT-JE+ALT-JE', '[BOLD] KFTT']
We can see that after combining with mixed fine tuning, our proposed methods also improve out-of-domain translation with the exception of IWSLT-CE. The reason for IWSLT-CE not improving as much as the JE language pairs is because this translation direction does not benefit from additional source side Chinese corpus. the appendix for the out-of-domain results.