  Recent works have shown that synthetic parallel data automatically generated
by translation models can be effective for various neural machine translation
(NMT) issues. In this study, we build NMT systems using only synthetic parallel
data. As an efficient alternative to real parallel data, we also present a new
type of synthetic parallel corpus. The proposed pseudo parallel data are
distinct from previous works in that ground truth and synthetic examples are
mixed on both sides of sentence pairs. Experiments on Czech-German and
French-German translations demonstrate the efficacy of the proposed pseudo
parallel corpus, which shows not only enhanced results for bidirectional
translation tasks but also substantial improvement with the aid of a ground
truth real parallel corpus.
