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Asking clarifying questions in response to search queries has been recognizedas a useful technique for revealing the underlying intent of the query.Clarification has applications in retrieval systems with different interfaces,from the traditional web search interfaces to the limited bandwidth interfacesas in speech-only and small screen devices. Generation and evaluation ofclarifying questions have been recently studied in the literature. However,user interaction with clarifying questions is relatively unexplored. In thispaper, we conduct a comprehensive study by analyzing large-scale userinteractions with clarifying questions in a major web search engine. In moredetail, we analyze the user engagements received by clarifying questions basedon different properties of search queries, clarifying questions, and theircandidate answers. We further study click bias in the data, and show that eventhough reading clarifying questions and candidate answers does not takesignificant efforts, there still exist some position and presentation biases inthe data. We also propose a model for learning representation for clarifyingquestions based on the user interaction data as implicit feedback. The model isused for re-ranking a number of automatically generated clarifying questionsfor a given query. Evaluation on both click data and human labeled datademonstrates the high quality of the proposed method.

Analyzing and Learning from User Interactions for Search Clarification