Fast Domain Adaptation for Neural Machine Translation
Table 6: Chinese→English adaptation results. The adaptation has been utilized on 593k in-domain parallel sentences.
['[BOLD] system', '[BOLD] epoch', '[BOLD] out-of-domain Bleu', '[BOLD] out-of-domain Ter', '[BOLD] in-domain Bleu', '[BOLD] in-domain Ter']
Because the in-domain data is relatively large in this case, training a NMT model from scratch only on the in-domain data gives us similar performance on the in-domain test set compared to the baseline model that is trained only on the out-of-domain data. However, the performance on the out-of-domain test set is significantly worse. By continuing the training of the baseline model only on the in-domain data, we get an improvement of 9.5 points in Bleu and 12.2 points in Ter on the in-domain test set after 6 epochs. Unfortunately, the performance significantly drops on the out-of-domain test set. After 20 epochs, the performance on the in-domain data only further improves slightly while losing much more on the out-of-domain test set.