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In an information-seeking conversation, a user may ask questions that areunder-specified or unanswerable. An ideal agent would interact by initiatingdifferent response types according to the available knowledge sources. However,most current studies either fail to or artificially incorporate such agent-sideinitiative. This work presents InSCIt, a dataset for Information-SeekingConversations with mixed-initiative Interactions. It contains 4.7K user-agentturns from 805 human-human conversations where the agent searches overWikipedia and either directly answers, asks for clarification, or providesrelevant information to address user queries. The data supports two subtasks,evidence passage identification and response generation, as well as a humanevaluation protocol to assess model performance. We report results of twosystems based on state-of-the-art models of conversational knowledgeidentification and open-domain question answering. Both systems significantlyunderperform humans, suggesting ample room for improvement in future studies.

INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions