Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity

Open-ended human learning and information-seeking are increasingly mediated by technologies like digital assistants. However, such systems often fail to account for the user's pre-existing knowledge, which is a powerful way to increase engagement and to improve retention. Assuming a correlation between engagement and user responses such as "liking" messages or asking followup questions, we design a Wizard of Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts that relate to their existing knowledge. Through crowd-sourcing of this experimental task we collected and now open-source 14K dialogs (181K utterances) where users and assistants converse about various aspects related to geographic entities. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, message grounding to Wikipedia, user reactions to messages, and per-dialog ratings. Our analysis shows that responses which incorporate a user's prior knowledge do increase engagement. We incorporate this knowledge into a state-of-the-art multi-task model that reproduces human assistant policies, improving over content selection baselines by 13 points.