Keywords: negotiation, dialogue, graph neural networks, interpretability, structure
Abstract: 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.
One-sentence Summary: We propose DialoGraph, a negotiation dialogue system that leverages Graph Attention Networks to model complex negotiation strategies while providing interpretability for the model via intermediate structures.
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Code: [![github](/images/github_icon.svg) rishabhjoshi/DialoGraph_ICLR21](https://github.com/rishabhjoshi/DialoGraph_ICLR21) + [![Papers with Code](/images/pwc_icon.svg) 1 community implementation](https://paperswithcode.com/paper/?openreview=kDnal_bbb-E)