annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
task_ids: []
paperswithcode_id: negotiation-dialogues-dataset
pretty_name: Deal or No Deal Negotiator
dataset_info:
- config_name: dialogues
features:
- name: input
sequence:
- name: count
dtype: int32
- name: value
dtype: int32
- name: dialogue
dtype: string
- name: output
dtype: string
- name: partner_input
sequence:
- name: count
dtype: int32
- name: value
dtype: int32
splits:
- name: train
num_bytes: 3860624
num_examples: 10095
- name: test
num_bytes: 396258
num_examples: 1052
- name: validation
num_bytes: 418491
num_examples: 1087
download_size: 5239072
dataset_size: 4675373
- config_name: self_play
features:
- name: input
sequence:
- name: count
dtype: int32
- name: value
dtype: int32
splits:
- name: train
num_bytes: 261512
num_examples: 8172
download_size: 98304
dataset_size: 261512
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 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
| train | validation | 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}
}
Contributions
Thanks to @moussaKam for adding this dataset.