Automated negotiation support systems aim to help human negotiators reach more favorable outcomes in multi-issue negotiations (e.g., an employer and a candidate negotiating over issues such as salary, hours, and promotions before a job offer). To be successful, these systems must accurately track agreements reached by participants in real-time. Existing approaches either focus on task-oriented dialogues or produce unstructured outputs, rendering them unsuitable for this objective. Our work introduces the novel task of agreement tracking for two-party multi-issue negotiations, which requires continuous monitoring of agreements within a structured state space. To address the scarcity of annotated corpora with realistic multi-issue negotiation dialogues, we use GPT-3 to build GPT-Negochat, a synthesized dataset that we make publicly available. We present a strong initial baseline for our task by transfer-learning a T5 model trained on the MultiWOZ 2.4 corpus. Pre-training T5-small and T5-base on MultiWOZ 2.4's DST task enhances results by 21% and 9% respectively over training solely on GPT-Negochat. We validate our method's sample-efficiency via smaller training subset experiments. By releasing GPT-Negochat and our baseline models, we aim to encourage further research in multi-issue negotiation dialogue agreement tracking.

翻译：自动化谈判支持系统旨在帮助人类谈判者在多议题谈判（例如，雇主与候选人在工作录用前就薪资、工时和晋升等议题进行谈判）中达成更有利的结果。为取得成功，这些系统必须实时准确追踪参与者已达成的协议。现有方法要么专注于任务导向型对话，要么产生非结构化输出，因此不适合此目标。我们的工作引入了双人多议题谈判中协议追踪的新任务，该任务要求在结构化状态空间中持续监控协议。针对缺乏标注语料库的现实多议题谈判对话，我们利用GPT-3构建了GPT-Negochat，这是一个我们公开提供的合成数据集。通过迁移学习在MultiWOZ 2.4语料库上训练的T5模型，我们为该任务提出了一个强有力的初始基线。在MultiWOZ 2.4的DST任务上预训练T5-small和T5-base后，相较于仅在GPT-Negochat上训练，结果分别提升了21%和9%。我们通过小规模训练子集实验验证了方法的样本效率。通过发布GPT-Negochat及我们的基线模型，我们旨在鼓励对多议题谈判对话协议追踪的进一步研究。