Dataset card

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": {}
+  }
+}