  We use Bayesian optimization to learn curricula for word representation
learning, optimizing performance on downstream tasks that depend on the learned
representations as features. The curricula are modeled by a linear ranking
function which is the scalar product of a learned weight vector and an
engineered feature vector that characterizes the different aspects of the
complexity of each instance in the training corpus. We show that learning the
curriculum improves performance on a variety of downstream tasks over random
orders and in comparison to the natural corpus order.
