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{
"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": {}
}
} |