Dataset: deal_or_no_dialog

# Dataset Card for Deal or No Deal Negotiator

## Dataset Description

### 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.

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 THEM: no . i want the hat and the balls YOU: both balls ? THEM: yeah or 1 ball and 1 book YOU: ok i want the hat and you can have the rest THEM: okay deal ill take the books and the balls you can have only the hat YOU: ok THEM: ', 'input': {'count': [3, 1, 2], 'value': [0, 8, 1]}, 'output': 'item0=0 item1=1 item2=0 item0=3 item1=0 item2=2', 'partner_input': {'count': [3, 1, 2], 'value': [1, 3, 2]}}

### 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

### Annotations

#### 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.

## Considerations for Using the Data

### 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}
}

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