@inproceedings{chawla-etal-2021-casino,
    title = "{C}a{S}i{N}o: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems",
    author = "Chawla, Kushal  and
      Ramirez, Jaysa  and
      Clever, Rene  and
      Lucas, Gale  and
      May, Jonathan  and
      Gratch, Jonathan",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.naacl-main.254",
    pages = "3167--3185",
    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",
}

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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","bibtex":"@inproceedings{chawla-etal-2021-casino,\n title = \"{C}a{S}i{N}o: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems\",\n author = \"Chawla, Kushal and\n Ramirez, Jaysa and\n Clever, Rene and\n Lucas, Gale and\n May, Jonathan and\n Gratch, Jonathan\",\n booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n month = jun,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2021.naacl-main.254\",\n pages = \"3167--3185\",\n 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\",\n}\n\n","author_short":["Chawla, K.","Ramirez, J.","Clever, R.","Lucas, G.","May, J.","Gratch, J."],"key":"chawla-etal-2021-casino","id":"chawla-etal-2021-casino","bibbaseid":"chawla-ramirez-clever-lucas-may-gratch-casinoacorpusofcampsitenegotiationdialoguesforautomaticnegotiationsystems-2021","role":"author","urls":{"Paper":"https://www.aclweb.org/anthology/2021.naacl-main.254"},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://jonmay.github.io/webpage/cutelabname/cutelabname.bib","dataSources":["hbZSwot2msWk92m5B","fcWjcoAgajPvXWcp7","GvHfaAWP6AfN6oLQE","j3Qzx9HAAC6WtJDHS","5eM3sAccSEpjSDHHQ"],"keywords":[],"search_terms":["casino","corpus","campsite","negotiation","dialogues","automatic","negotiation","systems","chawla","ramirez","clever","lucas","may","gratch"],"title":"CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems","year":2021}