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.

本文介绍了一种使用图神经网络来加入实用策略以进行有针对性的谈判的系统 DialoGraph，在训练过程中采用了一种新的图形表示方法，该方法能够更好地预测下一个最佳策略，并在精度和质量方面优于现有的谈判模型。