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• W2888914530 abstract "We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing $50) and the execution of that strategy (e.g., generating The bike is brand new. Selling for just $50.). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse di- alogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches." @default.
• W2888914530 created "2018-09-07" @default.
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• W2888914530 date "2018-08-29" @default.
• W2888914530 modified "2023-09-24" @default.
• W2888914530 title "Decoupling Strategy and Generation in Negotiation Dialogues" @default.
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• W2888914530 doi "https://doi.org/10.48550/arxiv.1808.09637" @default.
• W2888914530 hasPublicationYear "2018" @default.
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