Dataset Card for Deal or No Deal Negotiator
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Repository: Dataset Repository
- Paper: Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Dataset Summary
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.
Supported Tasks and Leaderboards
Train end-to-end models for negotiation
Languages
The text in the dataset is in English
Dataset Structure
Data Instances
{'dialogue': 'YOU: i love basketball and reading
Data Fields
dialogue
: The dialogue between the agents. input
: The input of the firt agent. partner_input
: The input of the other agent. count
: The count of the three available items. value
: The value of the three available items. output
: Describes how many of each of the three item typesare assigned to each agent
Data Splits
Tain | Valid | Test | |
---|---|---|---|
dialogues | 10095 | 1087 | 1052 |
self_play | 8172 | NA | NA |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
Human workers using Amazon Mechanical Turk. They were paid $0.15 per dialogue, with a $0.05 bonus for maximal scores. Only workers based in the United States with a 95% approval rating and at least 5000 previous HITs were used.
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
The project is licenced under CC-by-NC
Citation Information
@article{lewis2017deal,
title={Deal or no deal? end-to-end learning for negotiation dialogues},
author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},
journal={arXiv preprint arXiv:1706.05125},
year={2017}
}