  We present Edina, the University of Edinburgh's social bot for the Amazon
Alexa Prize competition. Edina is a conversational agent whose responses
utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative
new technique we call self-dialogues. These are conversations in which a single
AMT Worker plays both participants in a dialogue. Such dialogues are
surprisingly natural, efficient to collect and reflective of relevant and/or
trending topics. These self-dialogues provide training data for a generative
neural network as well as a basis for soft rules used by a matching score
component. Each match of a soft rule against a user utterance is associated
with a confidence score which we show is strongly indicative of reply quality,
allowing this component to self-censor and be effectively integrated with other
components. Edina's full architecture features a rule-based system backing off
to a matching score, backing off to a generative neural network. Our hybrid
data-driven methodology thus addresses both coverage limitations of a strictly
rule-based approach and the lack of guarantees of a strictly machine-learning
approach.
