Automated Support for Collective Memory of Conversational Interactions

Abstract

Maintaining consistency is a difficult challenge in crowd-powered systems in which constituent crowd workers may change over time. We discuss an initial outline for Chorus:Mnemonic, a system that augments the crowd’s collective memory of a conversation by automatically recovering past knowledge based on topic, allowing the system to support consistent multi-session interactions. We present the design of the system itself, and discuss methods for testing its effectiveness. Our goal is to provide consistency between long interactions with crowd-powered conversational assistants by using AI to augment crowd workers.