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