  An effective method to generate a large number of parallel sentences for
training improved neural machine translation (NMT) systems is the use of
back-translations of the target-side monolingual data. Recently, iterative
back-translation has been shown to outperform standard back-translation albeit
on some language pairs. This work proposes the iterative batch back-translation
that is aimed at enhancing the standard iterative back-translation and enabling
the efficient utilization of more monolingual data. After each iteration,
improved back-translations of new sentences are added to the parallel data that
will be used to train the final forward model. The work presents a conceptual
model of the proposed approach.
