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.

翻译：与人类谈判的代理商在教学和谈话性AI中发现广泛应用。 人体代理商谈判的多数努力依赖于限制性菜单驱动的通信界面。 为了推进基于语言的谈判系统研究,我们探索了早期预测买方-卖方谈判结果的新任务,其方式是该模型可以查阅的语句的分数不同。 我们探索了早期预测的可行性,方法是使用传统的基于特征的方法,以及使用句子模板将非语言性任务环境纳入预先培训的语言模式。 我们进一步量化语言特征在多大程度上有助于作出更好的预测,而不同于特定任务的价格信息。 最后,检验预先培训的模式帮助我们确定有助于预测业绩的具体特征,例如信任和协议。