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Automated systems that negotiate with humans have broad applications inpedagogy and conversational AI. To advance the development of practicalnegotiation systems, we present CaSiNo: a novel corpus of over a thousandnegotiation dialogues in English. Participants take the role of campsiteneighbors and negotiate for food, water, and firewood packages for theirupcoming trip. Our design results in diverse and linguistically richnegotiations while maintaining a tractable, closed-domain environment. Inspiredby the literature in human-human negotiations, we annotate persuasionstrategies and perform correlation analysis to understand how the dialoguebehaviors are associated with the negotiation performance. We further proposeand evaluate a multi-task framework to recognize these strategies in a givenutterance. We find that multi-task learning substantially improves theperformance for all strategy labels, especially for the ones that are the mostskewed. We release the dataset, annotations, and the code to propel future workin human-machine negotiations: https://github.com/kushalchawla/CaSiNo

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