Fast Domain Adaptation for Neural Machine Translation
Table 2: Adaptation results for the German→English translation task: tst2013 is the in-domain test set and newstest2014 is the out-of-domain test set. The combined data is the concatenation of the in-domain and out-of-domain training data.
['[BOLD] system', '[BOLD] epoch', '[BOLD] newstest2014 Bleu', '[BOLD] newstest2014 Ter', '[BOLD] tst2013 Bleu', '[BOLD] tst2013 Ter']
The translation quality of a NMT system trained only on the in-domain data is not satisfying. In fact, it performs even worse on both test sets compared to the baseline model which is only trained on the out-of-domain data. By continuing the training of the baseline model on the in-domain data only, we get a gain of 4.4 points in Bleu and 3.1 points in Ter on the in-domain test set tst2013 after the second epoch. Nevertheless, we lose 2.1 points in Bleu and 3.9 points in Ter on the out-of-domain test set newstest2014. After continuing the epoch for 20 epochs, the model tends to overfit and the performance of both test sets degrades.