  Building socialbots that can have deep, engaging open-domain conversations
with humans is one of the grand challenges of artificial intelligence (AI). To
this end, bots need to be able to leverage world knowledge spanning several
domains effectively when conversing with humans who have their own world
knowledge. Existing knowledge-grounded conversation datasets are primarily
stylized with explicit roles for conversation partners. These datasets also do
not explore depth or breadth of topical coverage with transitions in
conversations. We introduce Topical-Chat, a knowledge-grounded human-human
conversation dataset where the underlying knowledge spans 8 broad topics and
conversation partners don't have explicitly defined roles, to help further
research in open-domain conversational AI. We also train several
state-of-the-art encoder-decoder conversational models on Topical-Chat and
perform automated and human evaluation for benchmarking.
