  This paper introduces Doc2Bot, a novel dataset for building machines that
help users seek information via conversations. This is of particular interest
for companies and organizations that own a large number of manuals or
instruction books. Despite its potential, the nature of our task poses several
challenges: (1) documents contain various structures that hinder the ability of
machines to comprehend, and (2) user information needs are often
underspecified. Compared to prior datasets that either focus on a single
structural type or overlook the role of questioning to uncover user needs, the
Doc2Bot dataset is developed to target such challenges systematically. Our
dataset contains over 100,000 turns based on Chinese documents from five
domains, larger than any prior document-grounded dialog dataset for information
seeking. We propose three tasks in Doc2Bot: (1) dialog state tracking to track
user intentions, (2) dialog policy learning to plan system actions and
contents, and (3) response generation which generates responses based on the
outputs of the dialog policy. Baseline methods based on the latest deep
learning models are presented, indicating that our proposed tasks are
challenging and worthy of further research.
