Leveraging an established exercise in negotiation education, we build a novel dataset for studying how the use of language shapes bilateral bargaining. Our dataset extends existing work in two ways: 1) we recruit participants via behavioral labs instead of crowdsourcing platforms and allow participants to negotiate through audio, enabling more naturalistic interactions; 2) we add a control setting where participants negotiate only through alternating, written numeric offers.Despite the two contrasting forms of communication, we find that the average agreed prices of the two treatments are identical. But when subjects can talk, fewer offers are exchanged, negotiations finish faster, the likelihood of reaching agreement rises, and the variance of prices at which subjects agree drops substantially. We further propose a taxonomy of speech acts in negotiation and enrich the dataset with annotated speech acts. We set up prediction tasks to predict negotiation success and find that being reactive to the arguments of the other party is advantageous over driving the negotiation.

翻译：借助谈判教育中的一项经典练习，我们构建了一个新颖的数据集，用于研究语言使用如何塑造双边议价行为。我们的数据集在现有研究基础上实现了两点拓展：1）通过行为实验室而非众包平台招募参与者，并允许参与者通过音频进行谈判，从而促成更自然的互动；2）增设了一个对照情境，参与者仅通过交替进行的书面数值报价进行谈判。尽管这两种沟通形式截然不同，我们发现两种实验条件下的平均议定价格完全相同。但当参与者可以交谈时，交换的报价次数减少，谈判完成速度加快，达成协议的可能性上升，且参与者议定价格的方差显著下降。我们进一步提出了谈判中的言语行为分类体系，并用标注的言语行为丰富了数据集。我们设定了预测谈判成功的任务，并发现对对方论点的回应性比主导谈判更具优势。