{
  "overview": {
    "where": {
      "has-leaderboard": "no",
      "leaderboard-url": "N/A",
      "leaderboard-description": "N/A",
      "data-url": "[Github](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)",
      "website": "[Github](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)",
      "paper-url": "[Arxiv](https://arxiv.org/abs/2012.12458)",
      "paper-bibtext": "```\n@article{byrne2020tickettalk,\n  title={TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems},\n  author={Byrne, Bill and Krishnamoorthi, Karthik and Ganesh, Saravanan and Kale, Mihir Sanjay},\n  journal={arXiv preprint arXiv:2012.12458},\n  year={2020}\n}\n```",
      "contact-name": "Karthik Krishnamoorthi",
      "contact-email": "krishnamoorthi@google.com"
    },
    "languages": {
      "is-multilingual": "no",
      "license": "cc-by-4.0: Creative Commons Attribution 4.0 International",
      "task-other": "N/A",
      "language-names": [
        "English"
      ],
      "intended-use": "Dialogues",
      "license-other": "N/A",
      "task": "Dialog Response Generation",
      "communicative": "a movie ticketing dialog dataset with 23,789 annotated conversations. ",
      "language-dialects": "NA",
      "language-speakers": "NA"
    },
    "credit": {
      "organization-type": [
        "other"
      ],
      "organization-names": "NA",
      "creators": "Google researchers",
      "funding": "Google",
      "gem-added-by": "Tosin Adewumi (Lule\u00e5 University of Technology)"
    },
    "structure": {
      "data-fields": "- `gem_id`: The unique example id\n- `context`: The context of the conversation\n- `target`: A string representing the target\n-`references`: A List representing the target(s)\n-`conversation_id`: A unique ID of the conversation",
      "structure-description": "NA",
      "structure-labels": "NA",
      "structure-example": "```\n{'context': \"<PR>get_movie_attribute<PRAN>rating.movie<PRAV>rated R<C><U>I wanna see a movie<A>where are you?<U>spring hills kansas<PN>find_theaters<PAN>location<PAV>spring hills kansas<PR>find_theaters<PRAN>name.theater<PRAV>AMC Holiday Theater<PRAV>Cinemark Downtown<A>there are 2 theaters near you, the AMC Holiday Theater and Cinemark Downtown. Did you know which movie you'd like to see?<U>funny one please<PN>find_movies<PAN>location<PAV>spring hills kansas<PR>find_movies<PRAN>name.movie<PRAV>Not My Problem<PRAV>Family Jewels<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.genre<PR>get_movie_attribute<PRAN>name.genre<PRAV>comedy<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Matt Damon<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Noah Schnapp<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.genre<PR>get_movie_attribute<PRAN>name.genre<PRAV>romantic comedy<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Melissa McCarthy<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Ryan Reynolds<A>There's the comedy film called Not My Problem starring Matt Damon and Noah Schnapp. There's also a romantic comedy called Family Jewels starring Melissa McCarthy and Ryan Reynolds.<U>what ratings are there?<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>rating.movie<PR>get_movie_attribute<PRAN>rating.movie<PRAV>rated PG-13<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>rating.movie\",\n 'conversation_id': 'dlg-d1f52e7e-c34c-4e85-b406-85ed138b5068',\n 'gem_id': 'Taskmaster-train-0',\n 'references': ['Not My Problem is rated PG-13 and Family Jewels is rated R.'],\n 'target': 'Not My Problem is rated PG-13 and Family Jewels is rated R.'}\n```",
      "structure-splits": "-`train`: 187182 examples\n-`dev`: 23406 examples\n-`test`: 23316 examples",
      "structure-splits-criteria": "NA",
      "structure-outlier": "NA"
    },
    "what": {
      "dataset": "This is a large task-oriented dialog dataset in which a model has to produce the response. The input contains the context and a structured representation of what the model is supposed to generate. The input is already pre-formatted as string, turning this into a pure text-to-text problem. "
    }
  },
  "curation": {
    "original": {
      "is-aggregated": "no",
      "aggregated-sources": "N/A",
      "rationale": "NA",
      "communicative": "a movie ticketing dialog dataset with 23,789 annotated conversations."
    },
    "language": {
      "found": [],
      "crowdsourced": [
        "Participatory experiment"
      ],
      "created": "N/A",
      "machine-generated": "N/A",
      "validated": "not validated",
      "is-filtered": "not filtered",
      "filtered-criteria": "N/A",
      "obtained": [
        "Crowdsourced"
      ],
      "producers-description": "NA",
      "topics": "Ticketing",
      "pre-processed": "N/A"
    },
    "annotations": {
      "origin": "none",
      "rater-number": "N/A",
      "rater-qualifications": "N/A",
      "rater-training-num": "N/A",
      "rater-test-num": "N/A",
      "rater-annotation-service-bool": "no",
      "rater-annotation-service": [],
      "values": "N/A",
      "quality-control": [],
      "quality-control-details": "N/A"
    },
    "consent": {
      "has-consent": "no",
      "consent-policy": "N/A",
      "consent-other": "N/A",
      "no-consent-justification": "NA"
    },
    "pii": {
      "has-pii": "no PII",
      "no-pii-justification": "It's based on ticketing without personal information",
      "is-pii-identified": "N/A",
      "pii-identified-method": "N/A",
      "is-pii-replaced": "N/A",
      "pii-replaced-method": "N/A",
      "pii-categories": []
    },
    "maintenance": {
      "has-maintenance": "no",
      "description": "N/A",
      "contact": "N/A",
      "contestation-mechanism": "N/A",
      "contestation-link": "N/A",
      "contestation-description": "N/A"
    }
  },
  "gem": {
    "rationale": {
      "sole-task-dataset": "yes",
      "distinction-description": "NA",
      "contribution": "Dialogue generation that makes sense",
      "sole-language-task-dataset": "no",
      "model-ability": "NA"
    },
    "curation": {
      "has-additional-curation": "yes",
      "modification-types": [
        "other"
      ],
      "modification-description": "gem_id field was added to the 3 data splits",
      "has-additional-splits": "no",
      "additional-splits-description": "N/A",
      "additional-splits-capacicites": "N/A"
    },
    "starting": {
      "research-pointers": "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020",
      "technical-terms": "NA"
    }
  },
  "results": {
    "results": {
      "other-metrics-definitions": "N/A",
      "has-previous-results": "yes",
      "current-evaluation": "NA",
      "previous-results": "NA",
      "model-abilities": "BLEU: 60",
      "metrics": [
        "BLEU"
      ],
      "original-evaluation": "automatic evaluation"
    }
  },
  "considerations": {
    "pii": {
      "risks-description": "NA"
    },
    "licenses": {
      "dataset-restrictions-other": "N/A",
      "data-copyright-other": "N/A",
      "dataset-restrictions": [
        "open license - commercial use allowed"
      ],
      "data-copyright": [
        "public domain"
      ]
    },
    "limitations": {
      "data-technical-limitations": "NA",
      "data-unsuited-applications": "NA",
      "data-discouraged-use": "NA"
    }
  },
  "context": {
    "previous": {
      "is-deployed": "no",
      "described-risks": "N/A",
      "changes-from-observation": "N/A"
    },
    "underserved": {
      "helps-underserved": "no",
      "underserved-description": "N/A"
    },
    "biases": {
      "has-biases": "unsure",
      "bias-analyses": "N/A",
      "speaker-distibution": "NA"
    }
  }
}