We present ongoing research on using data cartography techniques to better understand the training dynamics of neural sequence-to-sequence models for Machine Translation (MT). This is particularly needed to inform the design of effective training algorithms for MT in low-resource settings. Current results show that the difficulty of training samples depends on the current training phase and raises questions for future work, i.e. what are the properties of these samples and how can we modify the training process at each phase.