  Progress in Machine Learning is often driven by the availability of large
datasets, and consistent evaluation metrics for comparing modeling approaches.
To this end, we present a repository of conversational datasets consisting of
hundreds of millions of examples, and a standardised evaluation procedure for
conversational response selection models using '1-of-100 accuracy'. The
repository contains scripts that allow researchers to reproduce the standard
datasets, or to adapt the pre-processing and data filtering steps to their
needs. We introduce and evaluate several competitive baselines for
conversational response selection, whose implementations are shared in the
repository, as well as a neural encoder model that is trained on the entire
training set.
