Building effective Neural Machine Translation models often implies accommodating diverse sets of heterogeneous data so as to optimize performance for the domain(s) of interest. Such multi-source / multi-domain adaptation problems are typically approached through instance selection or reweighting strategies, based on a static assessment of the relevance of training instances with respect to the task at hand. In this paper, we study dynamic data selection strategies that are able to automatically re-evaluate the usefulness of data samples in the course of training. Based on the results of multiple experiments, we show that our method offer a generic framework to automatically handle several real-world situations, from multi-source or unsupervised domain adaptation to multidomain learning.