Publication

Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Conference on Empirical Methods on Natural Language Processing (EMNLP)


Abstract

Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. 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. 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 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. Our code and dataset are publicly available.

Related Publications

All Publications

June 13, 2017

End-to-End Negotiator

No Authors Listed

Workshop on Online Abuse and Harms (WHOAH) at ACL - November 30, 2021

Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection

Lambert Mathias, Shaoliang Nie, Bertie Vidgen, Aida Davani, Zeerak Waseem, Douwe Kiela, Vinodkumar Prabhakaran

Journal of Big Data - November 6, 2021

A graphical method of cumulative differences between two subpopulations

Mark Tygert

NeurIPS - December 6, 2021

Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement

Samuel Daulton, Maximilian Balandat, Eytan Bakshy

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookie Policy