  Question answering (QA) over text passages is a problem of long-standing
interest in information retrieval. Recently, the conversational setting has
attracted attention, where a user asks a sequence of questions to satisfy her
information needs around a topic. While this setup is a natural one and similar
to humans conversing with each other, it introduces two key research
challenges: understanding the context left implicit by the user in follow-up
questions, and dealing with ad hoc question formulations. In this work, we
demonstrate CROWN (Conversational passage ranking by Reasoning Over Word
Networks): an unsupervised yet effective system for conversational QA with
passage responses, that supports several modes of context propagation over
multiple turns. To this end, CROWN first builds a word proximity network (WPN)
from large corpora to store statistically significant term co-occurrences. At
answering time, passages are ranked by a combination of their similarity to the
question, and coherence of query terms within: these factors are measured by
reading off node and edge weights from the WPN. CROWN provides an interface
that is both intuitive for end-users, and insightful for experts for
reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data,
where it achieved above-median performance in a pool of neural methods.
