The overall objective of 'social' dialogue systems is to support engaging,
entertaining, and lengthy conversations on a wide variety of topics, including
social chit-chat. Apart from raw dialogue data, user-provided ratings are the
most common signal used to train such systems to produce engaging responses. In
this paper we show that social dialogue systems can be trained effectively from
raw unannotated data. Using a dataset of real conversations collected in the
2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good'
system responses to user utterances, i.e. responses which are likely to lead to
long and engaging conversations. We show that (1) our neural ranker
consistently outperforms several strong baselines when trained to optimise for
user ratings; (2) when trained on larger amounts of data and only using
conversation length as the objective, the ranker performs better than the one
trained using ratings -- ultimately reaching a Precision@1 of 0.87. This
advance will make data collection for social conversational agents simpler and
less expensive in the future.