  Mixed initiative in open-domain dialogue requires a system to pro-actively
introduce new topics. The one-turn topic transition task explores how a system
connects two topics in a cooperative and coherent manner. The goal of the task
is to generate a "bridging" utterance connecting the new topic to the topic of
the previous conversation turn. We are especially interested in commonsense
explanations of how a new topic relates to what has been mentioned before. We
first collect a new dataset of human one-turn topic transitions, which we call
OTTers. We then explore different strategies used by humans when asked to
complete such a task, and notice that the use of a bridging utterance to
connect the two topics is the approach used the most. We finally show how
existing state-of-the-art text generation models can be adapted to this task
and examine the performance of these baselines on different splits of the
OTTers data.
