CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems

TL;DR

CaSiNo is a new English dialogue corpus of campsite negotiations designed to improve automated negotiation systems, featuring diverse interactions, annotated persuasion strategies, and a multi-task learning approach for strategy recognition.

Contribution

The paper introduces CaSiNo, a novel, richly annotated negotiation dialogue dataset with a multi-task framework for strategy recognition, advancing research in human-machine negotiation.

Findings

Multi-task learning improves strategy recognition accuracy.

Annotated persuasion strategies correlate with negotiation success.

Diverse, linguistically rich dialogues facilitate negotiation research.

Abstract

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