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Information needs around a topic cannot be satisfied in a single turn; userstypically ask follow-up questions referring to the same theme and a system mustbe capable of understanding the conversational context of a request to retrievecorrect answers. In this paper, we present our submission to the TRECConversational Assistance Track 2019, in which such a conversational setting isexplored. We propose a simple unsupervised method for conversational passageranking by formulating the passage score for a query as a combination ofsimilarity and coherence. To be specific, passages are preferred that containwords semantically similar to the words used in the question, and where suchwords appear close by. We built a word-proximity network (WPN) from a largecorpus, where words are nodes and there is an edge between two nodes if theyco-occur in the same passages in a statistically significant way, within acontext window. Our approach, named CROWN, improved nDCG scores over a providedIndri baseline on the CAsT training data. On the evaluation data for CAsT, ourbest run submission achieved above-average performance with respect to AP@5 andnDCG@1000.

CROWN: Conversational Passage Ranking by Reasoning over Word Networks