Agreement Tracking for Multi-Issue Negotiation Dialogues
Amogh Mannekote Bonnie J. Dorr Kristy Elizabeth Boyer
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.