  Domain adaptation has been well-studied in supervised neural machine
translation (SNMT). However, it has not been well-studied for unsupervised
neural machine translation (UNMT), although UNMT has recently achieved
remarkable results in several domain-specific language pairs. Besides the
inconsistent domains between training data and test data for SNMT, there
sometimes exists an inconsistent domain between two monolingual training data
for UNMT. In this work, we empirically show different scenarios for
unsupervised neural machine translation. Based on these scenarios, we revisit
the effect of the existing domain adaptation methods including batch weighting
and fine tuning methods in UNMT. Finally, we propose modified methods to
improve the performances of domain-specific UNMT systems.
