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Create dataset_card.json

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dataset_card.json ADDED
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+ {
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+ "overview": {
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+ "where": {
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+ "has-leaderboard": "no",
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+ "leaderboard-url": "N/A",
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+ "leaderboard-description": "N/A",
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+ "website": "http://abductivecommonsense.xyz/",
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+ "data-url": "https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip",
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+ "paper-url": "https://openreview.net/pdf?id=Byg1v1HKDB",
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+ "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}",
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+ "contact-name": "Chandra Bhagavatulla",
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+ "contact-email": "[email protected]"
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+ },
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+ "languages": {
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+ "is-multilingual": "no",
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+ "license": "apache-2.0: Apache License 2.0",
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+ "task-other": "N/A",
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+ "language-names": [
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+ "English"
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+ ],
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+ "language-speakers": "Crowdworkers on the Amazon Mechanical Turk platform based in the U.S, Canada, U.K and Australia. ",
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+ "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.",
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+ "license-other": "N/A",
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+ "task": "Reasoning"
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+ },
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+ "credit": {
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+ "organization-type": [
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+ "industry"
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+ ],
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+ "organization-names": "Allen Institute for AI",
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+ "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)",
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+ "funding": "Allen Institute for AI",
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+ "gem-added-by": "Chandra Bhagavatula (AI2), Ronan LeBras (AI2), Aman Madaan (CMU), Nico Daheim (RWTH Aachen University)"
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+ },
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+ "structure": {
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+ "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.",
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+ "structure-labels": "Explanations were authored by crowdworkers on the Amazon Mechanical Turk platform using a custom template designed by the creators of the dataset.",
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+ "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}",
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+ "structure-splits": "- train: Consists of training instances. \n- dev: Consists of dev instances.\n- test: Consists of test instances.\n"
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+ }
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+ },
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+ "gem": {
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+ "rationale": {
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+ "contribution": "Abductive reasoning is a crucial capability of humans and ART is the first dataset curated to study language-based abductive reasoning.",
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+ "sole-task-dataset": "no",
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+ "distinction-description": "N/A",
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+ "model-ability": "Whether models can reason abductively about a given pair of observations."
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+ },
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+ "curation": {
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+ "has-additional-curation": "no",
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+ "modification-types": [],
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+ "modification-description": "N/A",
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+ "has-additional-splits": "no",
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+ "additional-splits-description": "N/A",
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+ "additional-splits-capacicites": "N/A"
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+ },
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+ "starting": {
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+ "research-pointers": "- Paper: https://arxiv.org/abs/1908.05739\n- Code: https://github.com/allenai/abductive-commonsense-reasoning"
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+ }
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+ },
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+ "results": {
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+ "results": {
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+ "model-abilities": "Whether models can reason abductively about a given pair of observations.",
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+ "metrics": [
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+ "BLEU",
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+ "BERT-Score",
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+ "ROUGE"
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+ ],
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+ "other-metrics-definitions": "N/A",
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+ "has-previous-results": "no",
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+ "current-evaluation": "N/A",
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+ "previous-results": "N/A"
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+ }
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+ },
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+ "curation": {
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+ "original": {
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+ "is-aggregated": "no",
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+ "aggregated-sources": "N/A"
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+ },
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+ "language": {
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+ "obtained": [
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+ "Crowdsourced"
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+ ],
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+ "found": [],
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+ "crowdsourced": [
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+ "Amazon Mechanical Turk"
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+ ],
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+ "created": "N/A",
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+ "machine-generated": "N/A",
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+ "producers-description": "Language producers were English speakers in U.S., Canada, U.K and Australia.",
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+ "topics": "No",
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+ "validated": "validated by crowdworker",
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+ "pre-processed": "N/A",
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+ "is-filtered": "algorithmically",
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+ "filtered-criteria": "Adversarial filtering algorithm as described in the paper: https://arxiv.org/abs/1908.05739"
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+ },
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+ "annotations": {
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+ "origin": "automatically created",
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+ "rater-number": "N/A",
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+ "rater-qualifications": "N/A",
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+ "rater-training-num": "N/A",
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+ "rater-test-num": "N/A",
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+ "rater-annotation-service-bool": "no",
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+ "rater-annotation-service": [],
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+ "values": "Each observation is associated with a list of COMET (https://arxiv.org/abs/1906.05317) inferences.",
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+ "quality-control": "none",
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+ "quality-control-details": "N/A"
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+ },
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+ "consent": {
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+ "has-consent": "no",
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+ "consent-policy": "N/A",
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+ "consent-other": "N/A"
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+ },
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+ "pii": {
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+ "has-pii": "no PII",
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+ "no-pii-justification": "The dataset contains day-to-day events. It does not contain names, emails, addresses etc. ",
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+ "pii-categories": [],
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+ "is-pii-identified": "N/A",
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+ "pii-identified-method": "N/A",
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+ "is-pii-replaced": "N/A",
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+ "pii-replaced-method": "N/A"
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+ },
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+ "maintenance": {
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+ "has-maintenance": "no",
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+ "description": "N/A",
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+ "contact": "N/A",
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+ "contestation-mechanism": "N/A",
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+ "contestation-link": "N/A",
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+ "contestation-description": "N/A"
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+ }
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+ },
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+ "context": {
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+ "previous": {
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+ "is-deployed": "no",
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+ "described-risks": "N/A",
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+ "changes-from-observation": "N/A"
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+ },
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+ "underserved": {
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+ "helps-underserved": "no",
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+ "underserved-description": "N/A"
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+ },
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+ "biases": {
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+ "has-biases": "no",
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+ "bias-analyses": "N/A"
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+ }
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+ },
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+ "considerations": {
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+ "pii": {
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+ "risks-description": "None"
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+ },
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+ "licenses": {
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+ "dataset-restrictions": [
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+ "public domain"
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+ ],
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+ "dataset-restrictions-other": "N/A",
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+ "data-copyright": [
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+ "public domain"
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+ ],
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+ "data-copyright-other": "N/A"
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+ },
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+ "limitations": {}
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+ }
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+ }