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Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Proceedings of the 2017 Conference on Empirical Methods in Natural Language ProcessingĪssociation for Computational Linguistics

Our code and dataset are publicly available.",ĭeal or No Deal? End-to-End Learning of Negotiation Dialogues We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each others reward functions must reach an agreement (or a deal) via natural language dialogue. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. Publisher = "Association for Computational Linguistics",Ībstract = "Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Anthology ID: D17-1259 Volume: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing Month: September Year: 2017 Address: Copenhagen, Denmark Venue: EMNLP SIG: SIGDAT Publisher: Association for Computational Linguistics Note: Pages: 2443–2453 Language: URL: DOI: 10.18653/v1/D17-1259 Bibkey: lewis-etal-2017-deal Copy Citation: BibTeX MODS XML Endnote More options… PDF: Video: = "Deal or No Deal? End-to-End Learning of Negotiation Dialogues",īooktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", Our code and dataset are publicly available. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. Abstract Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions.