  The ability to engage in mixed-initiative interaction is one of the core
requirements for a conversational search system. How to achieve this is poorly
understood. We propose a set of unsupervised metrics, termed ConversationShape,
that highlights the role each of the conversation participants plays by
comparing the distribution of vocabulary and utterance types. Using
ConversationShape as a lens, we take a closer look at several conversational
search datasets and compare them with other dialogue datasets to better
understand the types of dialogue interaction they represent, either driven by
the information seeker or the assistant. We discover that deviations from the
ConversationShape of a human-human dialogue of the same type is predictive of
the quality of a human-machine dialogue.
