Multi-Modal Dialogue Policy Learning for Dynamic and Co-operative Goal Setting
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)(2021)
摘要
Developing an adequate and human-like virtual agent has been one of the primary applications of artificial intelligence. In the last few years, task-oriented dialogue systems have gained huge popularity because of their upsurging relevance and positive outcomes. In real-world, users may not always have a predefined and rigid task goal beforehand; they upgrade/downgrade/change their goal component dynamically depending upon their utility value and agent's serving capability. However, existing virtual agents fail to incorporate this dynamic behavior, leading to either unsuccessful task completion or an ungratified user experience. The paper presents an end to end multimodal dialogue system for dynamic and co-operative goal setting, which incorporates i) a multi-modal semantic state representation in policy learning to deal with multi-modal inputs, ii) a goal manager module in a traditional dialogue manager for handling dynamic and goal unavailability scenarios effectively, iii) an accumulative reward (task/persona/sentiment) for task success, personalized persuasion and user-adaptive behavior, respectively. The obtained experimental results and the comparisons with baselines firmly establish the need and efficacy of the proposed system.
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关键词
dynamic behavior,ungratified user experience,co-operative goal setting,multimodal semantic state representation,goal manager module,traditional dialogue manager,dynamic goal unavailability scenarios,user-adaptive behavior,multimodal dialogue policy learning,artificial intelligence,task-oriented dialogue systems,human-like virtual agent,multimodal dialogue system,dynamic goal setting,personalized persuasion
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