CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems
This work provides a dataset and framework to advance human-machine negotiation systems, but it is incremental as it builds on existing negotiation literature.
The authors tackled the problem of developing automated negotiation systems by creating CaSiNo, a corpus of over a thousand negotiation dialogues in a campsite scenario, and found that multi-task learning improved strategy recognition performance, especially for skewed labels.
Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip. Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closed-domain environment. Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance. We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance. We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed. We release the dataset, annotations, and the code to propel future work in human-machine negotiations: https://github.com/kushalchawla/CaSiNo