Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation
TABLE VI: Out-of-domain results (BLEU-4 scores) after multilingual multi-domain adaptation results (BLEU-4 scores) 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 is 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 low performance.
['[BOLD] Multiple Out-of-domain Corpora  [BOLD] No.', '[BOLD] Multiple Out-of-domain Corpora  [BOLD] KFTT-JE+IWSLT-JE+ALT-JE', '[BOLD] Multiple Out-of-domain Corpora  [BOLD] KFTT-JE', '[BOLD] Multiple Out-of-domain Corpora  [BOLD] IWSLT-JE', '[BOLD] Multiple Out-of-domain Corpora  [BOLD] IWSLT-CE']
We can see that the combination of our proposed methods with mixed fine tuning performs the best. For multilingual multi-domain adaptation, using multilingualism together with multi-domain data shows the best results for two out-of-domain translations, i.e., IWSLT-JE and IWSLT-CE. We also observe that combining our proposed methods with MFT has a positive impact on the relatively resource-rich IWSLT-JE and IWSLT-CE translation directions. In contrast, vanilla MFT does not achieve this kind of improvement. According to us, our methods learn better specialized representations or biases when provided with additional types of domains. Our results indicate that it is possible to package multiple language pairs and domains into a single NMT model with significant improvement for both in-domain and out-of-domain translations. the appendix for the out-of-domain results.