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        <title>Decoupling Strategy and Generation in Negotiation Dialogues</title>
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    <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 dialogue 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.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1256</identifier>
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%0 Conference Proceedings
%T Decoupling Strategy and Generation in Negotiation Dialogues
%A He, He
%A Chen, Derek
%A Balakrishnan, Anusha
%A Liang, Percy
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F he-etal-2018-decoupling
%X 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 dialogue 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.
%R 10.18653/v1/D18-1256
%U https://aclanthology.org/D18-1256
%U https://doi.org/10.18653/v1/D18-1256
%P 2333-2343

Markdown (Informal)

[Decoupling Strategy and Generation in Negotiation Dialogues](https://aclanthology.org/D18-1256) (He et al., EMNLP 2018)

• Decoupling Strategy and Generation in Negotiation Dialogues (He et al., EMNLP 2018)

ACL

• He He, Derek Chen, Anusha Balakrishnan, and Percy Liang. 2018. Decoupling Strategy and Generation in Negotiation Dialogues. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2333–2343, Brussels, Belgium. Association for Computational Linguistics.