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• W2788085213 abstract "User feedback can be an effective indicator to the success of the human-robot conversation. However, to avoid to interrupt the online real-time conversation process, explicit feedback is usually gained at the end of a conversation. Alternatively, users' responses usually contain their implicit feedback, such as stance, sentiment, emotion, etc., towards the conversation content or the interlocutors. Therefore, exploring the implicit feedback is a natural way to optimize the conversation generation process. In this paper, we propose a novel reward function which explores the implicit feedback to optimize the future reward of a reinforcement learning based neural conversation model. A simulation strategy is applied to explore the state-action space in training and test. Experimental results show that the proposed approach outperforms the Seq2Seq model and the state-of-the-art reinforcement learning model for conversation generation on automatic and human evaluations on the OpenSubtitles and Twitter datasets." @default.
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• W2788085213 date "2018-04-25" @default.
• W2788085213 modified "2023-09-27" @default.
• W2788085213 title "Exploring Implicit Feedback for Open Domain Conversation Generation" @default.
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• W2788085213 doi "https://doi.org/10.1609/aaai.v32i1.11253" @default.
• W2788085213 hasPublicationYear "2018" @default.
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