Many goal-oriented dialog agents are expected to identify slot-value pairs in a spoken query, then perform lookup in a knowledge base to complete the task. When the agent encounters unknown slotvalues, it may ask the user to repeat or reformulate the query. But a robust agent can proactively seek new knowledge from a user, to help reduce subsequent task failures. In this paper, we propose knowledge acquisition strategies for a dialog agent and show their effectiveness. The acquired knowledge can be shown to subsequently contribute to task completion.
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Copyright 2014 Association for Computational Linguistics