Analysing Clarification in Asynchronous Information-Seeking Conversations

What is it about?

This research analyses human-generated clarification questions to provide insights into how they are used to disambiguate and better understand information needs. A set of clarification questions is extracted from posts on the \emph{Stack Exchange} platform. Novel taxonomy is defined for the annotation of the questions and their responses. We investigate the clarification questions in terms of whether they add any information to the post (the initial question posted by the asker) and the accepted answer, which is the answer chosen by the asker. After identifying which clarification questions are more useful, we investigate the characteristics of these questions in terms of their types and patterns. Non-useful clarification questions are identified, and their patterns are compared with useful clarifications. Our analysis indicates that the most useful clarification questions have similar patterns, regardless of the topic. This research contributes to an understanding of clarification in conversations. It can provide insight for clarification dialogues in conversational search scenarios and the possible system generation of clarification requests in information seeking conversations.

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