

# Dataset Card for Deal or No Deal Negotiator

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Repository:** [Dataset Repository](https://github.com/facebookresearch/end-to-end-negotiator) 
- **Paper:** [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://arxiv.org/abs/1706.05125)

### 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 <eos> THEM: no . i want the hat and the balls <eos> YOU: both balls ? <eos> THEM: yeah or 1 ball and 1 book <eos> YOU: ok i want the hat and you can have the rest <eos> THEM: okay deal ill take the books and the balls you can have only the hat <eos> YOU: ok <eos> THEM: <selection>',
 '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](https://github.com/moussaKam) for adding this dataset.
task_categories:conversational annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:cc-by-4.0 arxiv:1706.05125 region:us
{"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}]}
TAGS
#task_categories-conversational #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1706.05125 #region-us 

Dataset Card for Deal or No Deal Negotiator
===========================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


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



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### 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


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


The project is licenced under CC-by-NC


### Contributions


Thanks to @moussaKam for adding this dataset.



### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### 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


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


The project is licenced under CC-by-NC ### Contributions


Thanks to @moussaKam for adding this dataset.
TAGS
#task_categories-conversational #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1706.05125 #region-us 
 ### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### 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


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


The project is licenced under CC-by-NC ### Contributions


Thanks to @moussaKam for adding this dataset.
passage: TAGS
#task_categories-conversational #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1706.05125 #region-us 
### 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



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations