| { | |
| "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": "[email protected]" | |
| }, | |
| "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" | |
| } | |
| } | |
| } |