  Back-translation provides a simple yet effective approach to exploit
monolingual corpora in Neural Machine Translation (NMT). Its iterative variant,
where two opposite NMT models are jointly trained by alternately using a
synthetic parallel corpus generated by the reverse model, plays a central role
in unsupervised machine translation. In order to start producing sound
translations and provide a meaningful training signal to each other, existing
approaches rely on either a separate machine translation system to warm up the
iterative procedure, or some form of pre-training to initialize the weights of
the model. In this paper, we analyze the role that such initialization plays in
iterative back-translation. Is the behavior of the final system heavily
dependent on it? Or does iterative back-translation converge to a similar
solution given any reasonable initialization? Through a series of empirical
experiments over a diverse set of warmup systems, we show that, although the
quality of the initial system does affect final performance, its effect is
relatively small, as iterative back-translation has a strong tendency to
convergence to a similar solution. As such, the margin of improvement left for
the initialization method is narrow, suggesting that future research should
focus more on improving the iterative mechanism itself.
