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{
"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"
}
}
} |