
# Dataset Card for Taskmaster-2

## 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-2-2020)
- **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 is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs
in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. 
Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs,
Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is
almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs.
All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced
workers played the role of a 'user' and trained call center operators played the role of the 'assistant'.
In this way, users were led to believe they were interacting with an automated system that “spoke”
using text-to-speech (TTS) even though it was in fact a human behind the scenes.
As a result, users could express themselves however they chose in the context of an automated interface.

### 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-0047a087-6a3c-4f27-b0e6-268f53a2e013",
    "instruction_id": "flight-6",
    "utterances": [
        {
            "index": 0,
            "segments": [],
            "speaker": "USER",
            "text": "Hi, I'm looking for a flight. I need to visit a friend."
        },
        {
            "index": 1,
            "segments": [],
            "speaker": "ASSISTANT",
            "text": "Hello, how can I help you?"
        },
        {
            "index": 2,
            "segments": [],
            "speaker": "ASSISTANT",
            "text": "Sure, I can help you with that."
        },
        {
            "index": 3,
            "segments": [],
            "speaker": "ASSISTANT",
            "text": "On what dates?"
        },
        {
            "index": 4,
            "segments": [
                {
                    "annotations": [
                        {
                            "name": "flight_search.date.depart_origin"
                        }
                    ],
                    "end_index": 37,
                    "start_index": 27,
                    "text": "March 20th"
                },
                {
                    "annotations": [
                        {
                            "name": "flight_search.date.return"
                        }
                    ],
                    "end_index": 45,
                    "start_index": 41,
                    "text": "22nd"
                }
            ],
            "speaker": "USER",
            "text": "I'm looking to travel from March 20th to 22nd."
        }
    ]
}
```

### 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

There are no deafults splits for all the config. The below table lists the number of examples in each config.

| Config            | Train  |
|-------------------|--------|
| flights           | 2481   |
| food-orderings    | 1050   |
| hotels            | 2355   |
| movies            | 3047   |
| music             | 1602   |
| restaurant-search | 3276   |
| sports            | 3478   |


## 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-2", "pretty_name": "Taskmaster-2", "dataset_info": [{"config_name": "flights", "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": 7073487, "num_examples": 2481}], "download_size": 23029880, "dataset_size": 7073487}, {"config_name": "food-ordering", "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": 1734825, "num_examples": 1050}], "download_size": 5376675, "dataset_size": 1734825}, {"config_name": "hotels", "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": 7436667, "num_examples": 2357}], "download_size": 22507266, "dataset_size": 7436667}, {"config_name": "movies", "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": 7112301, "num_examples": 3056}], "download_size": 21189893, "dataset_size": 7112301}, {"config_name": "music", "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": 2814030, "num_examples": 1603}], "download_size": 8981720, "dataset_size": 2814030}, {"config_name": "restaurant-search", "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": 7341998, "num_examples": 3276}], "download_size": 21472680, "dataset_size": 7341998}, {"config_name": "sports", "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": 5738818, "num_examples": 3481}], "download_size": 19549440, "dataset_size": 5738818}]}
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-2
=============================


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 is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs
in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports.
Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs,
Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is
almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs.
All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced
workers played the role of a 'user' and trained call center operators played the role of the 'assistant'.
In this way, users were led to believe they were interacting with an automated system that “spoke”
using text-to-speech (TTS) even though it was in fact a human behind the scenes.
As a result, users could express themselves however they chose in the context of an automated interface.


### 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


There are no deafults splits for all the config. The below table lists the number of examples in each config.



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 is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs
in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports.
Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs,
Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is
almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs.
All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced
workers played the role of a 'user' and trained call center operators played the role of the 'assistant'.
In this way, users were led to believe they were interacting with an automated system that “spoke”
using text-to-speech (TTS) even though it was in fact a human behind the scenes.
As a result, users could express themselves however they chose in the context of an automated interface. ### 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


There are no deafults splits for all the config. The below table lists the number of examples in each config.



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 is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs
in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports.
Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs,
Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is
almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs.
All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced
workers played the role of a 'user' and trained call center operators played the role of the 'assistant'.
In this way, users were led to believe they were interacting with an automated system that “spoke”
using text-to-speech (TTS) even though it was in fact a human behind the scenes.
As a result, users could express themselves however they chose in the context of an automated interface. ### 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


There are no deafults splits for all the config. The below table lists the number of examples in each config.



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.