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A peculiarity of conversational search systems is that they involvemixed-initiatives such as system-generated query clarifying questions.Evaluating those systems at a large scale on the end task of IR is verychallenging, requiring adequate datasets containing such interactions. However,current datasets only focus on either traditional ad-hoc IR tasks or queryclarification tasks, the latter being usually seen as a reformulation task fromthe initial query. The only two datasets known to us that contain both documentrelevance judgments and the associated clarification interactions are Qulac andClariQ. Both are based on the TREC Web Track 2009-12 collection, but cover avery limited number of topics (237 topics), far from being enough for trainingand testing conversational IR models. To fill the gap, we propose a methodologyto automatically build large-scale conversational IR datasets from ad-hoc IRdatasets in order to facilitate explorations on conversational IR. Ourmethodology is based on two processes: 1) generating query clarificationinteractions through query clarification and answer generators, and 2)augmenting ad-hoc IR datasets with simulated interactions. In this paper, wefocus on MsMarco and augment it with query clarification and answersimulations. We perform a thorough evaluation showing the quality and therelevance of the generated interactions for each initial query. This papershows the feasibility and utility of augmenting ad-hoc IR datasets forconversational IR.

Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search