Abstract

Information needs around a topic often cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme. 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 (CAsT) 2019, in which such a conversational setting is explored. We propose an 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, achieved above-average performance on the TREC CAsT data with respect to AP@5 and nDCG@1000.