Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing
Table 5: BLEU Scores with and without domain labels (DL) under equal model capacity.
['Method', 'Direct Training', 'w/o DL', 'with DL (Ours)']
We further shows that the performance gains are from the domain mixing methods, instead of from the new model architecture design. The only difference is that we remove domain label to guide the training of domain proportion, i.e., only L_{\rm gen} is used in the training loss, and L_{\rm mix} is removed. Training without domain labels shows a slight improvement over baseline, but is still significantly worse than our proposed method for most of the tasks. Therefore, we can conclude that our proposed domain mixing approach indeed improves performance.