  Neural ranking models are traditionally trained on a series of random
batches, sampled uniformly from the entire training set. Curriculum learning
has recently been shown to improve neural models' effectiveness by sampling
batches non-uniformly, going from easy to difficult instances during training.
In the context of neural Information Retrieval (IR) curriculum learning has not
been explored yet, and so it remains unclear (1) how to measure the difficulty
of training instances and (2) how to transition from easy to difficult
instances during training. To address both challenges and determine whether
curriculum learning is beneficial for neural ranking models, we need
large-scale datasets and a retrieval task that allows us to conduct a wide
range of experiments. For this purpose, we resort to the task of conversation
response ranking: ranking responses given the conversation history. In order to
deal with challenge (1), we explore scoring functions to measure the difficulty
of conversations based on different input spaces. To address challenge (2) we
evaluate different pacing functions, which determine the velocity in which we
go from easy to difficult instances. We find that, overall, by just
intelligently sorting the training data (i.e., by performing curriculum
learning) we can improve the retrieval effectiveness by up to 2%.
