
# Dataset Card for Taskmaster-1

## 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

- **Homepage:** [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/)
- **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019)
- **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** N/A
- **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com)

### Dataset Summary

Taskmaster-1 is a  goal-oriented conversational dataset. It includes 13,215 task-based
dialogs comprising six domains. Two procedures were used to create this collection,
each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach
in which trained agents and crowdsourced workers interact to complete the task while the second is
"self-dialog" in which crowdsourced workers write the entire dialog themselves.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

The dataset is in English language.

## Dataset Structure

### Data Instances

A typical example looks like this

```
{
    "conversation_id":"dlg-336c8165-068e-4b4b-803d-18ef0676f668",
    "instruction_id":"restaurant-table-2",
    "utterances":[
      {
        "index":0,
        "segments":[
          
        ],
        "speaker":"USER",
        "text":"Hi, I'm looking for a place that sells spicy wet hotdogs, can you think of any?"
      },
      {
        "index":1,
        "segments":[
          {
            "annotations":[
              {
                "name":"restaurant_reservation.name.restaurant.reject"
              }
            ],
            "end_index":37,
            "start_index":16,
            "text":"Spicy Wet Hotdogs LLC"
          }
        ],
        "speaker":"ASSISTANT",
        "text":"You might enjoy Spicy Wet Hotdogs LLC."
      },
      {
        "index":2,
        "segments":[
          
        ],
        "speaker":"USER",
        "text":"That sounds really good, can you make me a reservation?"
      },
      {
        "index":3,
        "segments":[
          
        ],
        "speaker":"ASSISTANT",
        "text":"Certainly, when would you like a reservation?"
      },
      {
        "index":4,
        "segments":[
          {
            "annotations":[
              {
                "name":"restaurant_reservation.num.guests"
              },
              {
                "name":"restaurant_reservation.num.guests"
              }
            ],
            "end_index":20,
            "start_index":18,
            "text":"50"
          }
        ],
        "speaker":"USER",
        "text":"I have a party of 50 who want a really sloppy dog on Saturday at noon."
      }
    ]
  }
```

### Data Fields

Each conversation in the data file has the following structure:

- `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
- `utterances`: A list of utterances that make up the conversation.
- `instruction_id`: A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation.

Each utterance has the following fields:

- `index`: A 0-based index indicating the order of the utterances in the conversation.
- `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
- `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
- `segments`: A list of various text spans with semantic annotations.

Each segment has the following fields:

- `start_index`: The position of the start of the annotation in the utterance text.
- `end_index`: The position of the end of the annotation in the utterance text.
- `text`: The raw text that has been annotated.
- `annotations`: A list of annotation details for this segment.

Each annotation has a single field:

- `name`: The annotation name.



### Data Splits

- one_person_dialogs

The data in `one_person_dialogs` config is split into `train`, `dev` and `test` splits.

|              |  train |  validation |  test |
|--------------|-------:|------------:|------:|
| N. Instances |   6168 |         770 |   770 |

- woz_dialogs

The data in `woz_dialogs` config has no default splits.

|              |  train |
|--------------|-------:|
| N. Instances |   5507 |


## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

[More Information Needed]

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

[More Information Needed]

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### 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 dataset is licensed under `Creative Commons Attribution 4.0 License`

### Citation Information

[More Information Needed]
```
@inproceedings{48484,
title	= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author	= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year	= {2019}
}
```
### Contributions

Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
task_categories:text-generation task_categories:fill-mask task_ids:dialogue-modeling annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:en license:cc-by-4.0 arxiv:1909.05358 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["dialogue-modeling"], "paperswithcode_id": "taskmaster-1", "pretty_name": "Taskmaster-1", "dataset_info": [{"config_name": "one_person_dialogs", "features": [{"name": "conversation_id", "dtype": "string"}, {"name": "instruction_id", "dtype": "string"}, {"name": "utterances", "list": [{"name": "index", "dtype": "int32"}, {"name": "speaker", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "segments", "list": [{"name": "start_index", "dtype": "int32"}, {"name": "end_index", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "annotations", "list": [{"name": "name", "dtype": "string"}]}]}]}], "splits": [{"name": "train", "num_bytes": 18037058, "num_examples": 6168}, {"name": "validation", "num_bytes": 2239656, "num_examples": 770}, {"name": "test", "num_bytes": 2224163, "num_examples": 770}], "download_size": 103276427, "dataset_size": 22500877}, {"config_name": "woz_dialogs", "features": [{"name": "conversation_id", "dtype": "string"}, {"name": "instruction_id", "dtype": "string"}, {"name": "utterances", "list": [{"name": "index", "dtype": "int32"}, {"name": "speaker", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "segments", "list": [{"name": "start_index", "dtype": "int32"}, {"name": "end_index", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "annotations", "list": [{"name": "name", "dtype": "string"}]}]}]}], "splits": [{"name": "train", "num_bytes": 13028593, "num_examples": 5507}], "download_size": 103276427, "dataset_size": 13028593}]}
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1909.05358 #region-us 

Dataset Card for Taskmaster-1
=============================


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
-------------------


* Homepage: Taskmaster-1
* Repository: GitHub
* Paper: Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
* Leaderboard: N/A
* Point of Contact: Taskmaster Googlegroup


### Dataset Summary


Taskmaster-1 is a goal-oriented conversational dataset. It includes 13,215 task-based
dialogs comprising six domains. Two procedures were used to create this collection,
each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach
in which trained agents and crowdsourced workers interact to complete the task while the second is
"self-dialog" in which crowdsourced workers write the entire dialog themselves.


### Supported Tasks and Leaderboards


### Languages


The dataset is in English language.


Dataset Structure
-----------------


### Data Instances


A typical example looks like this


### Data Fields


Each conversation in the data file has the following structure:


* 'conversation\_id': A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
* 'utterances': A list of utterances that make up the conversation.
* 'instruction\_id': A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation.


Each utterance has the following fields:


* 'index': A 0-based index indicating the order of the utterances in the conversation.
* 'speaker': Either USER or ASSISTANT, indicating which role generated this utterance.
* 'text': The raw text of the utterance. In case of self dialogs (one\_person\_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
* 'segments': A list of various text spans with semantic annotations.


Each segment has the following fields:


* 'start\_index': The position of the start of the annotation in the utterance text.
* 'end\_index': The position of the end of the annotation in the utterance text.
* 'text': The raw text that has been annotated.
* 'annotations': A list of annotation details for this segment.


Each annotation has a single field:


* 'name': The annotation name.


### Data Splits


* one\_person\_dialogs


The data in 'one\_person\_dialogs' config is split into 'train', 'dev' and 'test' splits.



* woz\_dialogs


The data in 'woz\_dialogs' config has no default 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?


### 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 dataset is licensed under 'Creative Commons Attribution 4.0 License'


### Contributions


Thanks to @patil-suraj for adding this dataset.



### Dataset Summary


Taskmaster-1 is a goal-oriented conversational dataset. It includes 13,215 task-based
dialogs comprising six domains. Two procedures were used to create this collection,
each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach
in which trained agents and crowdsourced workers interact to complete the task while the second is
"self-dialog" in which crowdsourced workers write the entire dialog themselves. ### Supported Tasks and Leaderboards ### Languages


The dataset is in English language.


Dataset Structure
----------------- ### Data Instances


A typical example looks like this ### Data Fields


Each conversation in the data file has the following structure:


* 'conversation\_id': A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
* 'utterances': A list of utterances that make up the conversation.
* 'instruction\_id': A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation.


Each utterance has the following fields:


* 'index': A 0-based index indicating the order of the utterances in the conversation.
* 'speaker': Either USER or ASSISTANT, indicating which role generated this utterance.
* 'text': The raw text of the utterance. In case of self dialogs (one\_person\_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
* 'segments': A list of various text spans with semantic annotations.


Each segment has the following fields:


* 'start\_index': The position of the start of the annotation in the utterance text.
* 'end\_index': The position of the end of the annotation in the utterance text.
* 'text': The raw text that has been annotated.
* 'annotations': A list of annotation details for this segment.


Each annotation has a single field:


* 'name': The annotation name. ### Data Splits


* one\_person\_dialogs


The data in 'one\_person\_dialogs' config is split into 'train', 'dev' and 'test' splits.



* woz\_dialogs


The data in 'woz\_dialogs' config has no default 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? ### 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 dataset is licensed under 'Creative Commons Attribution 4.0 License' ### Contributions


Thanks to @patil-suraj for adding this dataset.
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1909.05358 #region-us 
 ### Dataset Summary


Taskmaster-1 is a goal-oriented conversational dataset. It includes 13,215 task-based
dialogs comprising six domains. Two procedures were used to create this collection,
each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach
in which trained agents and crowdsourced workers interact to complete the task while the second is
"self-dialog" in which crowdsourced workers write the entire dialog themselves. ### Supported Tasks and Leaderboards ### Languages


The dataset is in English language.


Dataset Structure
----------------- ### Data Instances


A typical example looks like this ### Data Fields


Each conversation in the data file has the following structure:


* 'conversation\_id': A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
* 'utterances': A list of utterances that make up the conversation.
* 'instruction\_id': A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation.


Each utterance has the following fields:


* 'index': A 0-based index indicating the order of the utterances in the conversation.
* 'speaker': Either USER or ASSISTANT, indicating which role generated this utterance.
* 'text': The raw text of the utterance. In case of self dialogs (one\_person\_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
* 'segments': A list of various text spans with semantic annotations.


Each segment has the following fields:


* 'start\_index': The position of the start of the annotation in the utterance text.
* 'end\_index': The position of the end of the annotation in the utterance text.
* 'text': The raw text that has been annotated.
* 'annotations': A list of annotation details for this segment.


Each annotation has a single field:


* 'name': The annotation name. ### Data Splits


* one\_person\_dialogs


The data in 'one\_person\_dialogs' config is split into 'train', 'dev' and 'test' splits.



* woz\_dialogs


The data in 'woz\_dialogs' config has no default 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? ### 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 dataset is licensed under 'Creative Commons Attribution 4.0 License' ### Contributions


Thanks to @patil-suraj for adding this dataset.
passage: TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1909.05358 #region-us 
### Dataset Summary


Taskmaster-1 is a goal-oriented conversational dataset. It includes 13,215 task-based
dialogs comprising six domains. Two procedures were used to create this collection,
each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach
in which trained agents and crowdsourced workers interact to complete the task while the second is
"self-dialog" in which crowdsourced workers write the entire dialog themselves.### Supported Tasks and Leaderboards### Languages


The dataset is in English language.


Dataset Structure
-----------------### Data Instances


A typical example looks like this