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Abstract

Information needs around a topic cannot be satisfied in a single turn; users
typically ask follow-up questions referring to the same theme and a system must
be capable of understanding the conversational context of a request to retrieve
correct answers. In this paper, we present our submission to the TREC
Conversational Assistance Track 2019, in which such a conversational setting is
explored. We propose a simple unsupervised method for conversational passage
ranking by formulating the passage score for a query as a combination of
similarity and coherence. To be specific, passages are preferred that contain
words semantically similar to the words used in the question, and where such
words appear close by. We built a word-proximity network (WPN) from a large
corpus, where words are nodes and there is an edge between two nodes if they
co-occur in the same passages in a statistically significant way, within a
context window. Our approach, named CROWN, improved nDCG scores over a provided
Indri baseline on the CAsT training data. On the evaluation data for CAsT, our
best run submission achieved above-average performance with respect to AP@5 and
nDCG@1000.