DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues

Rishabh Joshi · Vidhisha Balachandran · Shikhar Vashishth · Alan Black · Yulia Tsvetkov

Keywords: [ negotiation ] [ dialogue ] [ structure ] [ interpretability ] [ graph neural networks ]

[ Abstract ]
[ Paper ]
Tue 4 May 9 a.m. PDT — 11 a.m. PDT


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.

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