  Intelligent selection of training data has proven a successful technique to
simultaneously increase training efficiency and translation performance for
phrase-based machine translation (PBMT). With the recent increase in popularity
of neural machine translation (NMT), we explore in this paper to what extent
and how NMT can also benefit from data selection. While state-of-the-art data
selection (Axelrod et al., 2011) consistently performs well for PBMT, we show
that gains are substantially lower for NMT. Next, we introduce dynamic data
selection for NMT, a method in which we vary the selected subset of training
data between different training epochs. Our experiments show that the best
results are achieved when applying a technique we call gradual fine-tuning,
with improvements up to +2.6 BLEU over the original data selection approach and
up to +3.1 BLEU over a general baseline.
