Domain Differential Adaptation for Neural Machine Translation
Table 4: Performance of ensembling different LMs on the law and medical datasets. LMs-general are trained with both in-domain and out-of-domain datasets.
['[BOLD] Strategy', '[BOLD] LAW-MED', '[BOLD] MED-LAW']
Ensembling multiple models is a common and broadly effective technique for machine learning, and one possible explanation for our success is that we are simply adding more models into the mix. To this end, we compare DDA-Deep with three models: the first one integrates NMT-out with two LMs-in trained with different random seeds and the second one integrates NMT-out with two LMs-out; we also integrate two general-domain LMs which are trained on both the in-domain and out-of-domain data and compare the performance.