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To successfully negotiate a deal, it is not enough to communicate fluently:pragmatic planning of persuasive negotiation strategies is essential. Whilemodern dialogue agents excel at generating fluent sentences, they still lackpragmatic grounding and cannot reason strategically. We present DialoGraph, anegotiation system that incorporates pragmatic strategies in a negotiationdialogue using graph neural networks. DialoGraph explicitly incorporatesdependencies between sequences of strategies to enable improved andinterpretable prediction of next optimal strategies, given the dialoguecontext. Our graph-based method outperforms prior state-of-the-art negotiationmodels both in the accuracy of strategy/dialogue act prediction and in thequality of downstream dialogue response generation. We qualitatively showfurther benefits of learned strategy-graphs in providing explicit associationsbetween effective negotiation strategies over the course of the dialogue,leading to interpretable and strategic dialogues.

DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues