Datasets:

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-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.

Tain Valid Test
N. Instances 6168 770 770
  • woz_dialogs

The data in woz_dialogs config has no default splits.

Tain
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 for adding this dataset.

Models trained or fine-tuned on taskmaster1

None yet