  Machine translation systems based on deep neural networks are expensive to
train. Curriculum learning aims to address this issue by choosing the order in
which samples are presented during training to help train better models faster.
We adopt a probabilistic view of curriculum learning, which lets us flexibly
evaluate the impact of curricula design, and perform an extensive exploration
on a German-English translation task. Results show that it is possible to
improve convergence time at no loss in translation quality. However, results
are highly sensitive to the choice of sample difficulty criteria, curriculum
schedule and other hyperparameters.
