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Asking clarifying questions in response to ambiguous or faceted queries hasbeen recognized as a useful technique for various information retrievalsystems, especially conversational search systems with limited bandwidthinterfaces. Analyzing and generating clarifying questions have been studiedrecently but the accurate utilization of user responses to clarifying questionshas been relatively less explored. In this paper, we enrich the representationslearned by Transformer networks using a novel attention mechanism from externalinformation sources that weights each term in the conversation. We evaluatethis Guided Transformer model in a conversational search scenario that includesclarifying questions. In our experiments, we use two separate external sources,including the top retrieved documents and a set of different possibleclarifying questions for the query. We implement the proposed representationlearning model for two downstream tasks in conversational search; documentretrieval and next clarifying question selection. Our experiments use a publicdataset for search clarification and demonstrate significant improvementscompared to competitive baselines.

Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search