Dataset:

Task Categories: sequence-modeling
Languages: en
Multilinguality: monolingual
Size Categories: 1K<n<10K
Licenses: cc-by-4.0
Language Creators: crowdsourced
Annotations Creators: crowdsourced
Source Datasets: original

Dataset Card Creation Guide

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 for adding this dataset.

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Models trained or fine-tuned on taskmaster2

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