  An effective method to generate a large number of parallel sentences for
training improved neural machine translation (NMT) systems is the use of the
back-translations of the target-side monolingual data. The standard
back-translation method has been shown to be unable to efficiently utilize the
available huge amount of existing monolingual data because of the inability of
translation models to differentiate between the authentic and synthetic
parallel data during training. Tagging, or using gates, has been used to enable
translation models to distinguish between synthetic and authentic data,
improving standard back-translation and also enabling the use of iterative
back-translation on language pairs that underperformed using standard
back-translation. In this work, we approach back-translation as a domain
adaptation problem, eliminating the need for explicit tagging. In the approach
-- \emph{tag-less back-translation} -- the synthetic and authentic parallel
data are treated as out-of-domain and in-domain data respectively and, through
pre-training and fine-tuning, the translation model is shown to be able to
learn more efficiently from them during training. Experimental results have
shown that the approach outperforms the standard and tagged back-translation
approaches on low resource English-Vietnamese and English-German neural machine
translation.
