DIALOGRAPH: INCORPORATING INTERPRETABLE STRATEGY-GRAPH NETWORKS INTO NEGOTIATION DIALOGUES

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. 1 

1. INTRODUCTION

Negotiation is ubiquitous in human interaction, from e-commerce to the multi-billion dollar sales of companies. Learning how to negotiate effectively involves deep pragmatic understanding and planning the dialogue strategically (Thompson; Bazerman et al., 2000b; Pruitt, 2013) . 



Code, data and a demo system is released at https://github.com/rishabhjoshi/ DialoGraph_ICLR21 1



Figure 1: Both options are equally plausible and fluent, but a response with effective pragmatic strategies leads to a better deal.Modern dialogue systems for collaborative tasks such as restaurant or flight reservations have made considerable progress by modeling the dialogue history and structure explicitly using the semantic content, like slot-value pairs(Larionov et al., 2018; Young, 2006), or implicitly with encoder-decoder architectures(Sordoni et al., 2015; Li  et al., 2016). In such tasks, users communicate explicit intentions, enabling systems to map the utterances into specific intent slots(Li et al., 2020). However, such mapping is less clear in complex non-collaborative tasks like negotiation (He et al., 2018) and persuasion(Wang et al.,  2019), where user intent and most effective strategies are hidden. Hence, along with the generated dialogue, the strategic choice of framing and the sequence of chosen strategies play a vital role, as depicted in Figure1. Indeed, prior work on negotiation dialogues has primarily focused on optimizing dialogue strategies-from highlevel task-specific strategies(Lewis et al., 2017), to more specific task execution planning(He et al., 2018), to fine-grained planning of linguistic outputs given

