Sentiment Aided Graph Attentive Contextualization for Task Oriented Negotiation Dialogue Generation

Abstract

Over the past several years, the demand and popularity of using virtual assistants to finish jobs like service scheduling and online shopping have increased. While keeping the user's request in mind, an effective task-oriented virtual agent must strive to improve the seller's profit. Therefore, in order to achieve the best possible trade-off between the parties, this form of virtual agent has to have strong negotiating abilities. Although current conversational agents are quite good at making fluent sentences, they are still unable to use strategic thinking. In order to more effectively contextualize the choice of the next set of negotiation methods while producing answers, we develop Nego-GAT, an end-to-end negotiation system that includes sentiment information and graph attention embedding into GPT-2. Our selfsupervised model outperforms earlier cuttingedge negotiation models in terms of both the precision of strategy/dialogue act prediction and the caliber of the generated dialogue responses 1 .