  A prerequisite for training corpus-based machine translation (MT) systems --
either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of
high-quality parallel data. This is arguably more important today than ever
before, as NMT has been shown in many studies to outperform SMT, but mostly
when large parallel corpora are available; in cases where data is limited, SMT
can still outperform NMT.
  Recently researchers have shown that back-translating monolingual data can be
used to create synthetic parallel corpora, which in turn can be used in
combination with authentic parallel data to train a high-quality NMT system.
Given that large collections of new parallel text become available only quite
rarely, backtranslation has become the norm when building state-of-the-art NMT
systems, especially in resource-poor scenarios.
  However, we assert that there are many unknown factors regarding the actual
effects of back-translated data on the translation capabilities of an NMT
model. Accordingly, in this work we investigate how using back-translated data
as a training corpus -- both as a separate standalone dataset as well as
combined with human-generated parallel data -- affects the performance of an
NMT model. We use incrementally larger amounts of back-translated data to train
a range of NMT systems for German-to-English, and analyse the resulting
translation performance.
