  Open-ended human learning and information-seeking are increasingly mediated
by digital assistants. However, such systems often ignore the user's
pre-existing knowledge. 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 related to what they know. Through
crowd-sourcing of this experiment, we collect and release 14K dialogs (181K
utterances) where users and assistants converse about geographic topics like
geopolitical entities and locations. This dataset is annotated with
pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia,
and user reactions to messages. Responses using a user's prior knowledge
increase engagement. We incorporate this knowledge into a multi-task model that
reproduces human assistant policies and improves over a BERT content model by
13 mean reciprocal rank points.
