Domain Differential Adaptation for Neural Machine Translation
Table 1: Translation accuracy (BLEU; Papineni et al. (2002)) under different settings. The first three rows list the language pair, the source domain, and the target domain. “LAW”, “MED” and “IT” represent law, medical and IT domains, respectively. We use compare-mt Neubig et al. (2019) to perform significance tests Koehn (2004) and statistical significance compared with the best baseline is indicated with ∗ (p<0.005) and † (p<0.05).
['[BOLD] Method', '[BOLD] De-En LAW', '[BOLD] De-En LAW', '[BOLD] De-En MED', '[BOLD] De-En MED', '[BOLD] De-En IT', '[BOLD] De-En IT', '[BOLD] Cs-En WMT', '[BOLD] De-En WMT']
Adapting Between Domains We can see the LM-Shallow model can only marginally improve and sometimes even harms the performance of baseline models. On the other hand, our proposed DDA-Shallow model can outperform the baseline significantly by over 2 BLEU points. This reinforces the merit of our main idea of explicitly modeling the difference between domains, instead of simply modeling the target domain itself.