| { | |
| "overview": { | |
| "where": { | |
| "has-leaderboard": "no", | |
| "leaderboard-url": "N/A", | |
| "leaderboard-description": "N/A", | |
| "website": "[Website](http://abductivecommonsense.xyz/)", | |
| "data-url": "[Google Storage](https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip)", | |
| "paper-url": "[OpenReview](https://openreview.net/pdf?id=Byg1v1HKDB)", | |
| "paper-bibtext": "```\n@inproceedings{\nBhagavatula2020Abductive,\ntitle={Abductive Commonsense Reasoning},\nauthor={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi},\nbooktitle={International Conference on Learning Representations},\nyear={2020},\nurl={https://openreview.net/forum?id=Byg1v1HKDB}\n}\n```", | |
| "contact-name": "Chandra Bhagavatulla", | |
| "contact-email": "chandrab@allenai.org" | |
| }, | |
| "languages": { | |
| "is-multilingual": "no", | |
| "license": "apache-2.0: Apache License 2.0", | |
| "task-other": "N/A", | |
| "language-names": [ | |
| "English" | |
| ], | |
| "language-speakers": "Crowdworkers on the Amazon Mechanical Turk platform based in the U.S, Canada, U.K and Australia. ", | |
| "intended-use": "To study the viability of language-based abductive reasoning. Training and evaluating models to generate a plausible hypothesis to explain two given observations.", | |
| "license-other": "N/A", | |
| "task": "Reasoning" | |
| }, | |
| "credit": { | |
| "organization-type": [ | |
| "industry" | |
| ], | |
| "organization-names": "Allen Institute for AI", | |
| "creators": "Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)", | |
| "funding": "Allen Institute for AI", | |
| "gem-added-by": "Chandra Bhagavatula (AI2), Ronan LeBras (AI2), Aman Madaan (CMU), Nico Daheim (RWTH Aachen University)" | |
| }, | |
| "structure": { | |
| "data-fields": "- `observation_1`: A string describing an observation / event.\n- `observation_2`: A string describing an observation / event.\n- `label`: A string that plausibly explains why observation_1 and observation_2 might have happened.", | |
| "structure-labels": "Explanations were authored by crowdworkers on the Amazon Mechanical Turk platform using a custom template designed by the creators of the dataset.", | |
| "structure-example": "```\n{\n'gem_id': 'GEM-ART-validation-0',\n'observation_1': 'Stephen was at a party.',\n'observation_2': 'He checked it but it was completely broken.',\n'label': 'Stephen knocked over a vase while drunk.'\n}\n```", | |
| "structure-splits": "- `train`: Consists of training instances. \n- `dev`: Consists of dev instances.\n- `test`: Consists of test instances.\n" | |
| }, | |
| "what": { | |
| "dataset": "Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation.\nThis data loader focuses on abductive NLG: a conditional English generation task for explaining given observations in natural language. " | |
| } | |
| }, | |
| "gem": { | |
| "rationale": { | |
| "contribution": "Abductive reasoning is a crucial capability of humans and ART is the first dataset curated to study language-based abductive reasoning.", | |
| "sole-task-dataset": "no", | |
| "distinction-description": "N/A", | |
| "model-ability": "Whether models can reason abductively about a given pair of observations." | |
| }, | |
| "curation": { | |
| "has-additional-curation": "no", | |
| "modification-types": [], | |
| "modification-description": "N/A", | |
| "has-additional-splits": "no", | |
| "additional-splits-description": "N/A", | |
| "additional-splits-capacicites": "N/A" | |
| }, | |
| "starting": { | |
| "research-pointers": "- [Paper](https://arxiv.org/abs/1908.05739)\n- [Code](https://github.com/allenai/abductive-commonsense-reasoning)" | |
| } | |
| }, | |
| "results": { | |
| "results": { | |
| "model-abilities": "Whether models can reason abductively about a given pair of observations.", | |
| "metrics": [ | |
| "BLEU", | |
| "BERT-Score", | |
| "ROUGE" | |
| ], | |
| "other-metrics-definitions": "N/A", | |
| "has-previous-results": "no", | |
| "current-evaluation": "N/A", | |
| "previous-results": "N/A" | |
| } | |
| }, | |
| "curation": { | |
| "original": { | |
| "is-aggregated": "no", | |
| "aggregated-sources": "N/A" | |
| }, | |
| "language": { | |
| "obtained": [ | |
| "Crowdsourced" | |
| ], | |
| "found": [], | |
| "crowdsourced": [ | |
| "Amazon Mechanical Turk" | |
| ], | |
| "created": "N/A", | |
| "machine-generated": "N/A", | |
| "producers-description": "Language producers were English speakers in U.S., Canada, U.K and Australia.", | |
| "topics": "No", | |
| "validated": "validated by crowdworker", | |
| "pre-processed": "N/A", | |
| "is-filtered": "algorithmically", | |
| "filtered-criteria": "Adversarial filtering algorithm as described in the [paper](https://arxiv.org/abs/1908.05739)" | |
| }, | |
| "annotations": { | |
| "origin": "automatically created", | |
| "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": "Each observation is associated with a list of COMET (https://arxiv.org/abs/1906.05317) inferences.", | |
| "quality-control": "none", | |
| "quality-control-details": "N/A" | |
| }, | |
| "consent": { | |
| "has-consent": "no", | |
| "consent-policy": "N/A", | |
| "consent-other": "N/A" | |
| }, | |
| "pii": { | |
| "has-pii": "no PII", | |
| "no-pii-justification": "The dataset contains day-to-day events. It does not contain names, emails, addresses etc. ", | |
| "pii-categories": [], | |
| "is-pii-identified": "N/A", | |
| "pii-identified-method": "N/A", | |
| "is-pii-replaced": "N/A", | |
| "pii-replaced-method": "N/A" | |
| }, | |
| "maintenance": { | |
| "has-maintenance": "no", | |
| "description": "N/A", | |
| "contact": "N/A", | |
| "contestation-mechanism": "N/A", | |
| "contestation-link": "N/A", | |
| "contestation-description": "N/A" | |
| } | |
| }, | |
| "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": "no", | |
| "bias-analyses": "N/A" | |
| } | |
| }, | |
| "considerations": { | |
| "pii": { | |
| "risks-description": "None" | |
| }, | |
| "licenses": { | |
| "dataset-restrictions": [ | |
| "public domain" | |
| ], | |
| "dataset-restrictions-other": "N/A", | |
| "data-copyright": [ | |
| "public domain" | |
| ], | |
| "data-copyright-other": "N/A" | |
| }, | |
| "limitations": {} | |
| } | |
| } |