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Search clarification has recently attracted much attention due to itsapplications in search engines. It has also been recognized as a majorcomponent in conversational information seeking systems. Despite itsimportance, the research community still feels the lack of a large-scale datafor studying different aspects of search clarification. In this paper, weintroduce MIMICS, a collection of search clarification datasets for real websearch queries sampled from the Bing query logs. Each clarification in MIMICSis generated by a Bing production algorithm and consists of a clarifyingquestion and up to five candidate answers. MIMICS contains three datasets: (1)MIMICS-Click includes over 400k unique queries, their associated clarificationpanes, and the corresponding aggregated user interaction signals (i.e.,clicks). (2) MIMICS-ClickExplore is an exploration data that includesaggregated user interaction signals for over 60k unique queries, each withmultiple clarification panes. (3) MIMICS-Manual includes over 2k unique realsearch queries. Each query-clarification pair in this dataset has been manuallylabeled by at least three trained annotators. It contains graded quality labelsfor the clarifying question, the candidate answer set, and the landing resultpage for each candidate answer.  MIMICS is publicly available for research purposes, thus enables researchersto study a number of tasks related to search clarification, includingclarification generation and selection, user engagement prediction forclarification, click models for clarification, and analyzing user interactionswith search clarification.

MIMICS: A Large-Scale Data Collection for Search Clarification