论文标题
探索买方销售商谈判成果的早期预测
Exploring Early Prediction of Buyer-Seller Negotiation Outcomes
论文作者
论文摘要
与人类谈判的代理商在教学法和对话AI中发现了广泛的应用。人类代理谈判中的大多数努力都取决于限制性菜单驱动的界面进行交流。为了推进基于语言的谈判系统的研究，我们通过改变模型可以访问的话语的一部分来探索早期预测买方谈判结果的新任务。我们通过使用传统的基于功能的方法以及使用句子模板将非语言任务上下文纳入预审计的语言模型，探索早期预测的可行性。我们进一步量化语言特征在多大程度上有助于做出更好的预测，除了特定于任务的价格信息。最后，探索验证的模型有助于我们确定有助于预测性能的特定特征，例如信任和协议。
Agents that negotiate with humans find broad applications in pedagogy and conversational AI. Most efforts in human-agent negotiations rely on restrictive menu-driven interfaces for communication. To advance the research in language-based negotiation systems, we explore a novel task of early prediction of buyer-seller negotiation outcomes, by varying the fraction of utterances that the model can access. We explore the feasibility of early prediction by using traditional feature-based methods, as well as by incorporating the non-linguistic task context into a pretrained language model using sentence templates. We further quantify the extent to which linguistic features help in making better predictions apart from the task-specific price information. Finally, probing the pretrained model helps us to identify specific features, such as trust and agreement, that contribute to the prediction performance.
