{
  "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": {}
  }
}