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README.md

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-{
-  "overview": {
-    "where": {
-      "has-leaderboard": "no",
-      "leaderboard-url": "N/A",
-      "leaderboard-description": "N/A",
-      "website": "http://abductivecommonsense.xyz/",
-      "data-url": "https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip",
-      "paper-url": "https://openreview.net/pdf?id=Byg1v1HKDB",
-      "paper-bibtext": "@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}",
-      "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'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}",
-      "structure-splits": "- train: Consists of training instances. \n- dev: Consists of dev instances.\n- test: Consists of test instances.\n"
-    }
-  },
-  "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": {}
-  }
-}