Exploring Multiparty Casual Talk for Social Human-Machine Dialogue

Much talk between humans is casual and multiparty. It facilitates social bonding and mutual co-presence rather than strictly being used to exchange information in order to complete well-defined practical tasks. Artificial partners that are capable of participating as a speaker or listener in such talk would be useful for companionship, educational, and social contexts. However, such applications require dialogue structure beyond simple question/answer routines. While there is body of theory on multiparty casual talk, there is a lack of work quantifying such phenomena. This is critical if we are to manage and generate human machine multiparty casual talk. We outline the current knowledge on the structure of casual talk, describe our investigations in this domain, summarise our findings on timing, laughter, and disfluency in this domain, and discuss how they can inform the design and implementation of truly social machine dialogue partners.