  Back-translation has proven to be an effective method to utilize monolingual
data in neural machine translation (NMT), and iteratively conducting
back-translation can further improve the model performance. Selecting which
monolingual data to back-translate is crucial, as we require that the resulting
synthetic data are of high quality and reflect the target domain. To achieve
these two goals, data selection and weighting strategies have been proposed,
with a common practice being to select samples close to the target domain but
also dissimilar to the average general-domain text. In this paper, we provide
insights into this commonly used approach and generalize it to a dynamic
curriculum learning strategy, which is applied to iterative back-translation
models. In addition, we propose weighting strategies based on both the current
quality of the sentence and its improvement over the previous iteration. We
evaluate our models on domain adaptation, low-resource, and high-resource MT
settings and on two language pairs. Experimental results demonstrate that our
methods achieve improvements of up to 1.8 BLEU points over competitive
baselines.
