Iterative Dual Domain Adaptation for Neural Machine Translation
Table 1: Experimental results on the Chinese-English translation task. † indicates statistically significantly better than (ρ<0.01) the result of WDCD.
['[BOLD] Model', 'TED Talk ( [BOLD] In-domain) Tst10', 'TED Talk ( [BOLD] In-domain) Tst11', 'TED Talk ( [BOLD] In-domain) Tst12', 'TED Talk ( [BOLD] In-domain) Tst13', 'TED Talk ( [BOLD] In-domain) Ave.', 'News ( [BOLD] Out-of-domain) Nist03', 'News ( [BOLD] Out-of-domain) Nist04', 'News ( [BOLD] Out-of-domain) Nist05', 'News ( [BOLD] Out-of-domain) Nist06', 'News ( [BOLD] Out-of-domain) Ave.']
On all test sets, our framework significantly outperforms other contrast models. Furthermore, we reach the following conclusions: Besides, we only chose MFT, KD, and WDCD as typical contrast models. This is because KD is the basic domain adaption approach of our framework, MFT and WDCD are the best domain adaptation method and multi-domain NMT model for comparison, respectively.