INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions

In an information-seeking conversation, a user converses with an agent to ask a series of questions that can often be under- or over-specified. An ideal agent would first identify that they were in such a situation by searching through their underlying knowledge source and then appropriately interacting with a user to resolve it. However, most existing studies either fail to or artificially incorporate such agent-side initiatives. In this work, we present INSCIT (pronounced Insight), a dataset for information-seeking conversations with mixed-initiative interactions. It contains a total of 4.7K user-agent turns from 805 human-human conversations where the agent searches over Wikipedia and either asks for clarification or provides relevant information to address user queries. We define two subtasks, namely evidence passage identification and response generation, as well as a new human evaluation protocol to assess the model performance. We report results of two strong baselines based on state-of-the-art models of conversational knowledge identification and open-domain question answering. Both models significantly underperform humans and fail to generate coherent and informative responses, suggesting ample room for improvement in future studies.