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IEEE Intelligent Systems
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The conversational agent understands and provides users with proper information based on natural language Conventional agents based on pattern matching have much restriction to manage various types of real dialogues and to improve the answering performance For the effective construction of conversational agents, we propose a domain-adaptive conversational agent that infers the user's intention with two-stage inference and incrementally improves the answering performance through a learning dialogue We can confirm the usefulness of the proposed method with examples and usability tests.