Specifically, in this paper we tackle the task of data-based, unsupervised adaptation, where representative methods include creation of a pseudoparallel corpus by back-translation of in-domain monolingual target sentences (Sennrich et al., 2016a), or construction of a pseudo-parallel indomain corpus by copying monolingual target sentences to the source side (Currey et al., 2017).
while these methods have potential to strengthen the target-language decoder through addition of in-domain target data, they do not explicitly provide direct supervision of domainspecific words, which we argue is one of the major difficulties caused by domain shift.