Decoupling Strategy and Generation in Negotiation Dialogues
He He, Derek Chen, Anusha Balakrishnan, Percy Liang · 2018
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.