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

Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, Dhruv Batra


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.
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:
https://www.aclweb.org/anthology/D17-1259
DOI:
10.18653/v1/D17-1259
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1259.pdf
Video:
 https://vimeo.com/238232142